Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 02 16 1589 1589 Hossein Arabi Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland Swagat Dash Department of Nuclear Medicine & Molecular Theranostics, Sarvodaya Hospital, Faridabad, India Habibollah Dadgar Department of Nuclear Medicine and Molecular imaging, Cancer Research Center, RAZAVI Hospital, Mashhad University of Medical Science, Mashhad, Iran Majid Assadi The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Research Institute, Department of Nuclear Medicine, Molecular Imaging, and Theranostics, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran 2026 02 14 Theranostics has revolutionized nuclear medicine by integrating diagnostic imaging and targeted radionuclide therapy, delivering precision oncology with proven survival benefits in cancers such as prostate cancer and neuroendocrine tumors. However, inter-patient variability in biodistribution, response, and toxicity remains a major challenge. This editorial explores the transformative potential of digital twins, dynamic virtual replicas of patients continuously updated with real-world data, as a natural synergy for theranostics. Theranostic digital twins enable predictive dosimetry, personalized treatment optimization, responder identification, and toxicity forecasting through hybrid AI-mechanistic models grounded in radiopharmacokinetics and radiobiology. Early development should prioritize clinically meaningful applications supported by comprehensive, harmonized multimodal datasets, robust hybrid modeling, and effective synchronization mechanisms. Large-scale collaboration and systematic evidence synthesis are essential to accelerate clinical translation. By bridging in-silico simulation with real-world theranostics, digital twins promise to evolve nuclear medicine toward truly proactive, equitable, and predictive personalized care. https://fbt.tums.ac.ir/index.php/fbt/article/view/1589 https://fbt.tums.ac.ir/index.php/fbt/article/download/1589/554

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 05 10 842 842 Hanie Arabian Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran Alireza Karimian Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran Hamid Reza Marateb Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, BarcelonaTech (UPC), Barcelona, Spain. Carolina Migliorelli Unit of Digital Health, Eurecat, Centre Tecnològic de Catalunya, 08005 Barcelona, Spain Miquel Angel Mañanas Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, BarcelonaTech (UPC), Barcelona, Spain Sergio Romero Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, BarcelonaTech (UPC), Barcelona, Spain Antonio Russi Epilepsy Unit, Hospital Quirón Teknon, Barcelona, Spain Rafał Nowak Magnetoencephalography Unit, Hospital Quirón Teknon, Barcelona, Spain 2023 10 08 2023 12 02 Abstract Purpose: Magnetoencephalography is the recording of magnetic fields resulting from the activities of brain neurons and provides the possibility of direct measurement of their activity in a non-invasive manner. Despite its high spatial and temporal resolution, magnetoencephalography has a weak amplitude signal, drastically reducing the signal-to-noise ratio in case of environmental noise. Therefore, signal reconstruction methods can be effective in recovering noisy and lost information. Materials and Methods: The magnetoencephalography signal of 11 healthy young subjects was recorded in a resting state. Each signal contains the data of 148 channels which were fixed on a helmet. The performance of three different reconstruction methods has been investigated by using the data of adjacent channels from the selected track to interpolate its information. These three methods are the surface reconstruction methods, partial differential equations algorithms, and finite element-based methods. Afterward to evaluate the performance of each method, R-square, root mean square error, and signal-to-noise ratio between the reconstructed signal and the original signal were calculated. The relation between these criteria was checked through proper statistical tests with a significance level of 0.05. Results: The mean method with the root mean square error of 0.016  0.009 (mean  SD) at the minimum time (3.5 microseconds) could reconstruct an epoch. Also, the median method with a similar error but in 5.9 microseconds with a probability of 99.33% could reconstruct an epoch with an R-square greater than 0.7. Conclusion: The mean and median methods can reconstruct the noisy or lost signal in magnetoencephalography with a suitable percentage of similarity to the reference by using the signal of adjacent channels from the damaged sensor. https://fbt.tums.ac.ir/index.php/fbt/article/view/842 https://fbt.tums.ac.ir/index.php/fbt/article/download/842/408

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 12 24 907 907 EN Siddhi Tamanekar Dwarkadas J. Sanghvi College of Engineering, Mumbai, India Zeenal Punamiya Medical Innovation Creativity and Entrepreneurship (M.I.C.E) Labs, Grant Government Medical College and Sir J.J Hospital, Mumbai, India 2023 12 16 2024 09 16 Purpose: The objective of this paper is to review the non-invasive methods for ICP monitoring and the research conducted in the field. Materials and Methods: A comprehensive literature search was conducted on NIH and PubMed, and papers highlighting the newer methods used in Intracranial Pressure monitoring were reviewed and the related data was included in the paper. Results:  The prominent methods of non-invasive ICP monitoring reviewed were: Imaging (CT and MRI), Electroencephalogram (EEG), Near-Infrared Spectroscopy (NIRS), Optic Nerve Sheath Diameter (ONSD), and Transcranial Doppler (TCD) Ultrasound. Conclusion: While invasive methods for ICP monitoring are preferred over non-invasive methods in a clinical setting, with the intraventricular catheter being the gold standard for ICP monitoring, many non-invasive methods for ICP monitoring are considered, especially in settings where invasive ICP monitoring is not possible. The use of non-invasive methods represents an advancement in the field of ICP monitoring. Although not very well known in a clinical setting, non-invasive methods offer more safety and carry a lesser risk of infection. https://fbt.tums.ac.ir/index.php/fbt/article/view/907 https://fbt.tums.ac.ir/index.php/fbt/article/download/907/470

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 12 22 1130 1130 Mahshid Kiani tehran university of medical siences Saeed Farzanefar tehran university of medical siences Seyyed Soheila Mirabedian tehran university of medical siences Mohsen Bakhshi Kashi tehran university of medical siences Elisabeth Eppard Otto von Guericke University (OvGU) Nasim Vahidfar tehran university of medical siences 2024 10 17 2024 12 04 Radiopharmaceuticals are combinations of two main components, a pharmaceutical component that targets specific moieties, and a radionuclide component that acts through spontaneous degradation for diagnostic, therapeutic purposes, or both simultaneously known as theranostics. By combining diagnostic and therapeutic methods, radiotheranostics play an important role in reducing radiation dosages for patients, increasing treatment effectiveness, controlling side effects, improving patient outcomes, and reducing overall treatment costs. Despite the diagnostic and therapeutic roles, radiopharmaceuticals are beneficial for assessing prognosis, disease progression and possibility of recurrences, treatment planning strategies, and assessing response to treatment. The most incredible role of radiopharmacy is establishing new radiopharmaceuticals to better target and tolerated agents for imaging and treatment in a clinic. These approaches are supported by nuclear medicine non-invasive procedures. It is crucial for radiopharmaceuticals that drug delivery occurs in a highly selective and sensitive manner to minimize the potential radiation risk to patients. This report will provide an overview of the recent progress in radiopharmaceuticals for diagnosis and therapy, including the latest radiotheranostic tracers, key concerns within the field, and future trends and prospects. Additionally, the available and useful radiopharmaceuticals are categorized into separate tables based on their specific characteristics. Presenting information in table format enhances organization and makes the data more understandable and accessible for users. This structured approach allows users to quickly locate relevant information, compare different radiopharmaceuticals, and grasp essential details at a glance. By utilizing tables, we ensure that critical information is not only easy to read but also effectively highlights the unique attributes of each radiopharmaceutical, ultimately improving the decision-making process for healthcare professionals. https://fbt.tums.ac.ir/index.php/fbt/article/view/1130 https://fbt.tums.ac.ir/index.php/fbt/article/download/1130/466

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 09 08 1019 1019 Asraa Sabah Abtan Pediatric and Preventive Dentistry Department, College of Dentistry, University of Baghdad, Baghdad, Iraq https://orcid.org/0009-0006-7520-1212 *Corresponding author: asraasabah90@gmail.com Nibal Mohammad Hoobi Pediatric and Preventive Dentistry Department, College of Dentistry, University of Baghdad, Baghdad, Iraq 2024 05 28 2024 07 24 Purpose: The present study aimed to evaluate the effectiveness of incorporating of nanohydroxyapatite in to hydrogen peroxide bleaching material on color, microhardness and morphological features of dental enamel. Materials and Methods: 33 sound maxillary first premolar were used for the study. Enamel blocks (7mm× 5mm×3mm) were prepared from the middle third of buccal halves of each tooth. Each dental block was embedded in self-curing acrylic resin with exterior enamel surface exposed for various applications. The dental blocks were  randomly divided into three groups (n=11) according to the bleaching technique. The groups were designed as follows: control; hydrogen peroxide (HP) and hydrogen peroxide with nanohydroxyapatite (HP-nHAp) groups. Color measurements and microhardness tests were conducted before and after treatment. one sample represented each group was selected for morphological analysis. Results: The  results showed  that both HP and HP-nHAp groups induced color changing. Enamel microhardness loss of HP group was significantly higher than that of HP-nHAp and control groups. The enamel morphological changes was only observed in HP group. Conclusion: nHAp could significantly reduce the enamel microharness loss caused by HP while preserving enamel surface morphological features without affecting bleaching efficacy. https://fbt.tums.ac.ir/index.php/fbt/article/view/1019 https://fbt.tums.ac.ir/index.php/fbt/article/download/1019/439

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 04 22 1227 1227 Cobra Ghasemi 1Rehabilitation Research Center, Department of Physiotherapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran. Fatemeh Taslimi Department of Internal Medicine, School of Medicine, Mazandaran University of Medical Science, Mazandaran, Iran. Zahra Masoumi Verki Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2025 02 16 2025 03 04 Background: This review aims to synthesize current literature on recent advances in the diagnosis and treatment of Brain and spinal cord injuries (SCIs), focusing on molecular imaging, cell therapy, brain-computer interfaces (BCIs), and craniosacral therapy (CST). Methods: A systematic search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, and Google Scholar to identify relevant articles published between 2015 and 2025. Keywords included "Brain Injury," "Spinal Cord Injury," "Molecular Imaging," "Cell Therapy," "Brain-Computer Interface," and "Craniosacral Therapy." Results: Molecular imaging techniques, such as fMRI, DTI, and PET, enhance diagnostic accuracy by visualizing neural activity and structural integrity. Cell therapy, particularly with mesenchymal stem cells (MSCs), shows promise in promoting axon regeneration and reducing inflammation. BCIs offer potential for restoring motor function and enhancing neural plasticity. The evidence for CST is mixed, with some studies suggesting benefits in pain relief and cognitive improvement, while others raise concerns about methodological limitations. Conclusion: Recent advances in molecular imaging, cell therapy, and BCIs offer promising avenues for improving the diagnosis and treatment of BSCI. However, further rigorous research is needed to validate the efficacy of these approaches and to address ethical considerations. While CST has gained attention as a complementary therapy, more high-quality studies are required to determine its effectiveness. This review highlights the need for interdisciplinary collaboration to translate scientific discoveries into clinical practice and to improve the quality of life for individuals affected by BSCI. https://fbt.tums.ac.ir/index.php/fbt/article/view/1227 https://fbt.tums.ac.ir/index.php/fbt/article/download/1227/497

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 12 29 1084 1084 Raheleh Zarei Lemraski Research Center for Prevention of Oral and Dental Diseases, Baqiyatallah University of Medical Sciences 2024 08 08 2024 09 24 Purpose: This case report aimed to describe a treatment for severe inflammatory external root resorption (RR). Materials and Methods: A 13-year-old boy reported the avulsion of his upper left central incisor. The tooth had been avulsed four months prior and was replanted forty minutes later by an emergency service. The canal was thoroughly irrigated with 2% sodium hypochlorite and then filled with calcium hydroxide of a creamy consistency as an intracanal medication due to its antimicrobial properties, using lentulo spirals. The calcium hydroxide was left inside the canal for a month. Results: Following the diagnosis, treatment involved conventional endodontic therapy with calcium hydroxide dressings, and the root canal was definitively filled after radiographic control of the resorption. At the 6- and 12-month follow-ups, clinical and radiographic examinations revealed no signs or symptoms of any abnormalities. The resorption process had halted, and the radiograph showed the reappearance of the normal lamina dura, indicating successful therapy. Conclusion: This case report details the treatment of severe external inflammatory RR in a tooth undergoing orthodontic treatment. Successful tooth replantation depends on the effective implementation of the recommended therapy. However, when inflammatory external RR occurs, appropriate endodontic treatment is necessary to eliminate necrotic tissue and bacteria, along with the use of calcium hydroxide dressings. https://fbt.tums.ac.ir/index.php/fbt/article/view/1084 https://fbt.tums.ac.ir/index.php/fbt/article/download/1084/471

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 10 28 1371 1371 Fatemeh Zare Department of Medical Physics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran Zahra Masoumi Verki Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Mina Nouri Department of Radiology Technology, School of Paramedical Sciences, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran Amirhossein Rashnoodi Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Emad Khoshdel Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Başak Göksel Department of Medical Services and Techniques, Vocational School of Health Sciences, Istanbul Gelisim University, Istanbul, Turkey Fatemeh Ghamkhar-Nakhjiri Department of Medical Physics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran Reza Malekzadeh Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran 2025 07 11 2025 10 03 Background: The aim of this study is to provide a comprehensive review of recent advances in the application of nanocarriers for targeted drug delivery and radiosensitization in cancer radiotherapy (RT), as well as to examine the challenges, solutions, and future prospects of this technology. Methods: A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and Embase, identifying 373 records. Following PRISMA guidelines, 36 studies met inclusion criteria focusing on functionalized nanocarriers in cancer RT. Data extraction covered nanoparticle types, functionalization, therapeutic payloads, cancer models, radiation modalities, and outcomes. Results: Forty studies were analyzed, categorized into iron oxide-based (10), silver (10), bismuth-based (7), graphene-based (4), gadolinium-based (4), and titanium-based (2) nanoparticles (NPs). Bismuth-based NPs (BiNPs) showed superior radiosensitization with sensitizer enhancement ratios (SERs) of 1.25–1.48 and up to 450% reactive oxygen species (ROS) increase in vivo, achieving ~70% tumor volume reduction without systemic toxicity. Silver NPs (AgNPs) demonstrated dose enhancement factors (DEF) rising from 1.4 to 1.9 and synergistic effects with docetaxel plus 2 Gy radiation. Iron oxide NPs functionalized with HER2 and RGD ligands reduced cell viability by 1.95-fold and achieved DEF of 89.1 in targeted systems. Gadolinium NPs reached SERs up to 2.44 at 65 keV, while graphene-based systems enhanced ROS production by 75.2%. Titanium-based NPs increased ROS levels 2.5-fold. Combination therapies integrating chemotherapeutics such as cisplatin and curcumin with nanocarriers yielded SERs up to 4.29. Radiation modalities included megavoltage X-rays (4–10 MV, n=24), synchrotron keV X-rays (n=2), gamma rays (0.38–1.25 MeV, n=3), and electron beams (6–12 MeV, n=3). Conclusions: Bismuth-based NPs represent the most promising radiosensitizers due to their high efficacy, safety, and clinical relevance, supporting their advancement toward clinical translation. https://fbt.tums.ac.ir/index.php/fbt/article/view/1371 https://fbt.tums.ac.ir/index.php/fbt/article/download/1371/534

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 09 08 918 918 Vandana Bavkar Bhivarabai Sawant College of Engineering & Research, Narhe, Pune, India Arundhati A. Shinde Bharati Vidyapeeth (Deemed to be University), 2024 01 05 2024 07 26 Purpose: Diabetes, resulting from insufficient insulin production or utilization, causes extensive harm to the body. The conventional diagnostic methods are often invasive. The classification of diabetes is essential for effective management. The progression in research and technology has led to additional classification approaches. Machine Learning (ML) algorithms have been deployed for analyzing the huge dataset and classifying diabetes. Materials and Methods: The classification and the regression of diabetic and non-diabetic are performed using the XGBoost mechanism. On the other hand, the proposed class-centric Focal XG-Boost is applied to elevate the model performance by measuring the similarity among the features. The prediction of the model is based on the classification and regression rates of diabetic and non-diabetic individuals, which are anticipated using applicable and effectual metrics to estimate their working performance. The dataset used in the Class-Centric Focal XG Boost model is attained using the Arduino Uno Kit. The data collection is done under a sampling rate of 100 Hz. The data are gathered from Bharati Hospital Pathology Laboratories, located in Pune. Results: The inclusive outcomes of the proposed model with their appropriate Exploratory Data Analysis (EDA) among classification and regression, with the suitable dataset used in the study are exemplified. Conclusion: The proposed Class-Centric Focal XG Boost model has numerous advantages and is less delicate to the hyperparameters than the conventional XGBoost algorithm. As a part of the real-time application of the Class-Centric Focal XG Boost model, the model can be utilized in other communicable and communicable disease classification and detection. https://fbt.tums.ac.ir/index.php/fbt/article/view/918 https://fbt.tums.ac.ir/index.php/fbt/article/download/918/440

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 04 22 1226 1226 Yalda Alimorad Khoram abad Department of Nuclear physics, Faculty of Basic Sciences, University of Mohaghegh Ardabili, Ardabil, Iran. Zahra Masoumi Verki Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Narjes Sabokro Department of Medical physics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. 2025 02 14 2025 03 04 Abstract Objective: To evaluate the effectiveness of polymer-based shields containing boron compounds for radiation protection in medical centers, focusing on their performance against neutron and gamma radiation. Methods: A comprehensive literature review was conducted using databases including PubMed, Scopus, Web of Science, and Embase. Studies published from 2010 to February 2025 were included. The search strategy employed keywords related to polymer-based shields, boron compounds, and radiation protection in medical settings. Results: Boron-containing polymers demonstrated significant potential for radiation shielding, particularly against neutrons. Nanocomposites incorporating high-Z elements showed improved gamma radiation attenuation. Hexagonal boron nitride (h-BN) nanocomposites exhibited superior neutron absorption properties. Epoxy-based composites with various nanoparticles showed enhanced protection against both neutron and gamma radiation. Recycled high-density polyethylene (R-HDPE) composites containing gadolinium oxide demonstrated promising thermal neutron shielding capabilities. Conclusion: Polymer-based shields containing boron compounds offer lightweight, flexible, and effective alternatives to traditional shielding materials. These materials show particular promise in medical applications, potentially improving safety for both patients and healthcare providers. However, challenges remain in optimizing material composition, thickness, and long-term stability for practical implementation in clinical settings. https://fbt.tums.ac.ir/index.php/fbt/article/view/1226 https://fbt.tums.ac.ir/index.php/fbt/article/download/1226/498

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 10 16 939 939 Seyyed Ali Zendehbad Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran Hamid Reza Kobravi Research Center of Biomedical Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran Mohammad Mahdi Khalilzadeh Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran Athena Sharifi Razavi Clinical Research Development Unit of Bou Ali Sina Hospital, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran Payam Sasan Nezhad Ghaem Medical Center, Department of Neurology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran 2024 02 10 2024 07 28 Purpose: In this study, we propose a novel generalizable hybrid underlying mechanism m for mapping Human Pose Estimation (HPE) data to muscle synergy patterns, which can be highly efficient in improving visual biofeedback. Materials and Methods: In the first step, Electromyography (EMG) data from the upper limb muscles of twelve healthy participants are collected and pre-processed, and muscle synergy patterns are extracted from it. Concurrently, kinematic data are detected using the OpenPose model. Through synchronization and normalization, the Successive Variational Mode Decomposition (SVMD) algorithm decomposes synergy control patterns into smaller components. To establish mappings, a custom Bidirectional Gated Recurrent Unit (BiGRU) model is employed. Comparative analysis against popular models validates the efficacy of our approach, revealing the generated trajectory as potentially ideal for visual biofeedback. Remarkably, the combined SVMD-BiGRU model outperforms alternatives. Results: the results show that the trajectory generated by the model is potentially suitable for visual biofeedback systems. Remarkably, the combined SVMD-BiGRU model outperforms alternatives. Furthermore, empirical assessments have demonstrated the adept ability of healthy participants to closely adhere to the trajectory generated by the model output during the test phase. Conclusion: Ultimately, the incorporation of this innovative mechanism at the heart of visual biofeedback systems has been revealed to significantly elevate both the quantity and quality of movement. https://fbt.tums.ac.ir/index.php/fbt/article/view/939 https://fbt.tums.ac.ir/index.php/fbt/article/download/939/451

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 02 05 1582 1582 Fereshteh Yousefirizi Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC, Canada Jean-Mathieu Beauregard Department of Radiology and Nuclear Medicine; and Cancer Research Centre, Université Laval, Quebec City, QC, Canada Arman Rahmim Departments of Physics and Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada 2026 01 25 2026 01 31 Quantitative molecular imaging is central to treatment response assessment in oncology, yet clinical practice remains largely dominated by patient-level or limited target-lesion criteria that ignore inter-lesion heterogeneity. This limitation is particularly important in prostate cancer, where PSMA PET/CT can reveal extensive skeletal and nodal metastatic disease that often evolves heterogeneously under therapy. Accurate and scalable lesion segmentation and tracking across serial PSMA PET/CT and post-therapy SPECT/CT scans is therefore essential for implementing emerging PSMA-specific response frameworks, such as RECIP 1.0, and for enabling lesion-level dosimetry in 177Lu-PSMA radiopharmaceutical therapies (RPTs). This article examines clinical motivations, technical foundations, and future pathways for automated lesion tracking in prostate cancer imaging. We focus on the unique requirements introduced by PSMA PET/CT compared with FDG PET/CT and highlight the critical role of quantitative SPECT/CT in linking imaging-derived disease characterization with delivered therapeutic dose. Recent advances in AI-based segmentation and automated lesion matching now make scalable longitudinal lesion correspondence feasible, providing comprehensive infrastructure for standardized response assessment and personalized PSMA-based theranostics. https://fbt.tums.ac.ir/index.php/fbt/article/view/1582 https://fbt.tums.ac.ir/index.php/fbt/article/download/1582/552

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 10 20 1101 1101 Mahboobeh Mehrabifard Department of Radiology, Faculty of Paramedicine, Hormozgan University of Medical Sciences, Bandarabas, Iran. Solmaz Derakhshan Department of Internal Medicine, Kosar Hospital, Kordestan University of Medical Sciences, Sanandaj, Iran Roqaie Kalantari Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. Amirhossein Rashnoodi Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Saadat Ebrahimiyan Department of Medical Physics and Radiology, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran Sahar Mohammadjani Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. Omid Talaee Department of Nuclear Engineering, Faculty of Mechanical Engineering, Shiraz University, Shiraz, Iran Navid Kheradmand Department of Health Physics, Graduate School of Health Sciences, İstanbul Medipol University, İstanbul, Türkiye 2024 09 03 2024 09 19 Purpose: Integrating magnetic Nanoparticles (NPs) into contrast-enhanced Magnetic Resonance (MR) imaging can significantly improve the resolution and sensitivity of the resulting images, leading to enhanced accuracy and reliability in diagnostic information. The present study aimed to investigate the use of targeted trastuzumab-labeled iron oxide (TZ-PEG-Fe3O4) NPs to enhance imaging capabilities for the detection and characterization of Breast Cancer (BC) cells. Materials and Methods: The NPs were synthesized by loading Fe3O4NPs with the monoclonal antibody TZ. Initially, Fe3O4 NPs were produced and subsequently coated with Polyethylene Glycol (PEG) to form PEG- Fe3O4 NPs. The TZ antibody was then conjugated to the PEG- Fe3O4 NPs, resulting in TZ-PEG-Fe3O4 NPs. The resulting NPs were characterized using standard analytical techniques, including UV-Vis spectroscopy, FTIR, SEM, TEM, VSM, and assessments of colloidal stability. Results: Analyses indicated that the targeted TZ-PEG-Fe3O4 NPs exhibited a spherical morphology and a relatively uniform size distribution, with an average diameter of approximately 60 nm. These results confirmed the successful synthesis and controlled fabrication of the Fe3O4 NPs, which is crucial for developing effective Contrast Agents (CAs) for medical imaging applications. Additionally, the study confirmed the biocompatibility and magnetic properties of the synthesized TZ-PEG-Fe3O4 NPs. Conclusion: The findings suggest that the developed targeted TZ-PEG-Fe3O4 NPs have significant potential as effective CAs for MR imaging of BC cells. https://fbt.tums.ac.ir/index.php/fbt/article/view/1101 https://fbt.tums.ac.ir/index.php/fbt/article/download/1101/454

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 03 1614 1614 Hamid Abdollahi Department of Radiology, University of British Columbia, Vancouver, Canada Ahmad Fayaz-Bakhsh Self-Care Academic Research Unit (SCARU), Department of Primary Care & Public Health, School of Public Health, Imperial College, London, UK Ivan Klyuzhin Ascinta Technologies, Vancouver, Canada Madjid Soltani Department of Basic and Translation Research, BC Cancer Research institute, Vancouver, Canada Babak Saboury Department of Basic and Translation Research, BC Cancer Research institute, Vancouver, Canada Arman Rahmim Departments of Physics & Biomedical Engineering, University of British Columbia, Vancouver, Canada 2026 04 23 2026 04 29 Theranostics is emerging as a powerful modality in precision oncology, integrating diagnostic imaging with targeted therapies to enable more effective and individualized cancer management. In parallel, artificial intelligence (AI) and digital twin (DT) technologies are increasingly being explored as enabling frameworks for advancing research, education, and clinical decision support. AI facilitates a range of quantitative and workflow-driven tasks, including organ and lesion segmentation, longitudinal lesion matching and tracking, and absorbed dose estimation, while also contributing to evidence generation, implementation, and evaluation processes. Complementing this, DTs integrate multimodal images, pharmacokinetic models, molecular characteristics, and clinical data to create dynamic, patient-specific representations of disease and treatment response. Together, these technologies improve treatment response and outcome prediction, enhance treatment planning, and support more adaptive and data-informed disease management strategies. In the near term, clinical practitioners and trainees must learn to effectively supervise AI systems, understand algorithmic limitations, and ensure their safe and effective use within clinical workflows. Over time, DT-enabled environments may support immersive, simulation-based learning with continuous feedback and exposure to complex or rare clinical scenarios, reshaping professional training. More broadly, the convergence of AI and DT technologies is driving an evolution in the structure of oncology practice itself. Alongside the four established clinical specialties medical oncology, radiation oncology, surgical oncology, and nuclear oncology a complementary role is emerging: computational oncology. These clinical and computational roles operate synergistically within an integrated health system, advancing data-driven and patient-centered precision oncology. https://fbt.tums.ac.ir/index.php/fbt/article/view/1614 https://fbt.tums.ac.ir/index.php/fbt/article/download/1614/559

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 11 30 1128 1128 Saleh Salehi Zahabi Department of Medical Physics, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran Saeed Bagherzadeh Department of Medical Physics, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran Karim Khoshgard Department of Medical Physics, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran Ali MohammadSharifi Clinical Research Development Center, Shahid Modarres Educational Hospital, Shahid Beheshti University of Medical Science, Tehran, Iran Afsane Mir Derikvand Department of Medical Physics, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran 2024 10 15 2024 11 13 Background: The prevalence of coronavirus has increased the use of CT scans, a high-exposure imaging technique. This study was designed to estimate organ dose and effective dose to investigate the lifetime attributable risks (LARs) of cancer incidence and mortality in COVID-19 patients. 600 patients who had COVID-19 or were suspected of having it, were included in the current study. Methods: Dosimetric parameters such as dose length product (DLP), volumetric CT dose index (CTDIV), and scan length, were used to estimate the patient’s dose and cancer risk. The ImPACT CT dosimetry software was also used to calculate organ doses and effective doses. The cancer risk was calculated using the National Academy of Sciences' Biologic Effects of Ionizing Radiation (BEIR VII) report. Results: For females, the mean effective dose based on International Commission Radiation Protection 103 (ICRP103) and ICRP 60 was 2.36 ± 0.48 mSv and 1.2 ± 0.28 mSv, respectively. For males, this parameter was 2.31 ± 0.53 mSv and 1.21 ± 0.45 mSv based on ICRP103 and ICRP60, respectively. For males, the mean LAR of all cancer incidence and cancer mortality was 14.79 ± 4.85 and 8.59 ± 2.42 per 100000 people, respectively. For females, these parameters were 23.37 ± 9.59 and 12.61± 3.89 per 100000 people, respectively. Conclusion: Chest CT scan examination connected with a non-considerable radiation dose and risk of cancer. So according to the ALARA principle, CT protocol must be optimized to limit radiation-induced risk. https://fbt.tums.ac.ir/index.php/fbt/article/view/1128 https://fbt.tums.ac.ir/index.php/fbt/article/download/1128/462

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 01 05 1017 1017 Ghazal Etemadi Department of Medical Physics and Biomedical Engineering, Shahid Beheshti University of Medical Sciences, Tehran, Iran Ahmad Mostaar Payam Azadeh Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Niloofar Yousefi Moteghaed Department of Medical Physics and Biomedical Engineering, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2024 05 25 2024 10 15 Purpose: The phantom-less patient-specific quality assurance (PSQA) for intensity‐modulated radiotherapy (IMRT) plan verification has been exploited recently. The aim of this study was the feasibility of the PSQA of the plan based on a log file and onboard detector for prostate patients in helical tomotherapy. Method: For 15 prostate patients, the quality assurance (QA) of the helical tomotherapy plan was performed using the Delta4 phantom and Cheese phantom to evaluate the spatial dose distribution and point dose, respectively. These parameters were also reconstructed by delivery analysis (DA) software using the measured leaf open times (LOTs). The gamma analysis and relative dose difference were used to compare the measured and reconstructed dose with the calculated values. Then, using the relative discrepancy, the log file and onboard detector data were compared with the expected data to assess machine performance. Results: The mean relative dose difference was within 1.3% among the measurement, reconstruction, and calculation. The results of statistical analysis and p-value showed there is no statistically significant difference in determining the dose difference between the DA-based and conventional QA methods. The gamma values of 3%/3mm, 3%/2mm, 2%/3mm, 2%/2mm, 2%/1mm, and 1%/1mm for the DA-based QA method were the same as the measurement QA method. However, the gamma values of 3%/1mm, 1%/3mm, and 1%/2mm were comparable. The mean percentage difference LOTs was 0.07%, and most differences occurred in very low and some high LOTs. The relative difference was lower than 2.30% for the couch speed, couch movement, monitor unit, and rotation per minute (RPM) gantry between the log file and expected data. Conclusion: The DA software is an efficient alternative to the measurement-based PSQA method. However, the accuracy of the DA software requires further investigations for gamma analysis at strict criteria. The very low and high LOTs may lead to the dose discrepancy. The tomotherapy machine can accurately implement the planned parameters. https://fbt.tums.ac.ir/index.php/fbt/article/view/1017 https://fbt.tums.ac.ir/index.php/fbt/article/download/1017/474

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 01 13 1013 1013 Abdulwahab M Al-Qaraghuli B.D.S, Ministry of Health and M.Sc. student at Conservative Department, Mustansiriyah University/College of Dentistry, Baghdad-Iraq Abdulwahab.mustafa@uomustansiriyah.edu.iq Id ORCID: 0009-0004-0541-4840 Mohammed K. Gholam Conservative Department, Mustansiriyah University/College of Dentistry, Baghdad-Iraq 2024 05 13 2024 06 09 Purpose: Evaluating the impact of various surface treatments on the adhesive strength between resin cement and zirconia surface. Material and methods: Using an STL file, 60 monolithic zirconia discs (Vita YZ HT) with dimensions of 10 millimeters in diameter and 2 millimeters in height were produced. They were machined, sintered, and the surface was smoothed using 600, 800, and 1200 grit aluminum oxide paper. Four groups were created based on the surface treatment applied to the discs: no treatment (control), sandblasting, potassium hydrogen difluoride and Zircos-E solution. Resin cement cylinders (Panavia V5; Kuraray Noritake) were applied on zirconia discs using a custom mold. The shear bond strength was assessed subsequent to thermocycling. The scanning electron microscope (SEM) has been utilised to analyse the morphological alterations of a specimen from every group. A post-hoc Tukey's test (P < 0.05) and a two-way ANOVA were used to statistically analyse the data. Results: The data analysis showed that the maximum shear bond strength values, measured at 128.933 ± 2.764Mpa, were obtained via airborne particle abrasion with 50-µm Al2O3. The values obtained by the control group were the lowest, at 50.933 ± 9.573 Mpa. The use of 50-µm Al2O3 in airborne particle abrasion caused a significant increase in shear bond strength values (p<0.05). Conclusion: The adhesive strength between zirconia and resin cement was improved by surface treatments, and airborne particle abrasion with 50-µm Al2O3 was shown to be an effective way to increase bond strength. https://fbt.tums.ac.ir/index.php/fbt/article/view/1013 https://fbt.tums.ac.ir/index.php/fbt/article/download/1013/478

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 02 09 901 901 Soheyla Sharifian Jazi Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran Saman Dalvand Department of Medical Physics, School of Medical Sciences, Tarbiat Modares University Hamed Zamani Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran Fahimeh Hossein Beigi 0000-0002-8113-3475 Mohammad Ghaderian Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran Reihaneh Faraji Department of Medical Physics, Medicine Faculty, Mashhad University of Medical Sciences, Mashhad, Iran Daryoush Shahbazi-Gahrouei Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 2023 12 14 2024 06 28 Abstract Purpose: The dose of Computed tomography (CT) scan exams consists of a large proportion of all medical imaging modalities’ dose burdens. There are different methods to measure and describe radiation in CT. A standardized way is to measure the Computed Tomography Dose Index (CTDI). However, due to the increase in the detector system size along the z-axis in new CT scanners generations, new measurement methods are described in the American Association of Physicists in Medicine-Task Group No.111(AAPM-TG111). This study aims to estimate the equilibrium dose and compare it with the dose displayed in the volume computed tomography dose index (CTDIvol) at the end of each exam. Eventually, the effective dose was calculated for both methods. Material and Methods: Using standard phantom of polymethylmethacrylate (PMMA) and pencil ionization chamber, the values of CTDI100, ( CTD100), CTDIvol, cumulative dose, equilibrium dose, and effective dose were calculated. Results: Six protocols performed in two centers and the results indicated that the measurements with a standard CT dosimetry phantom, was varied between average equilibrium dose and CTDIvol and the discrepancies ranged between 26% to 35%. Conclusion: the CTDIVol is not suitable to evaluate the radiation dose at the end of each scan and the use of an equilibrium dose for dosimetry of new systems is recommended. Keywords: Multidetector computed tomography, Equilibrium dose, Computed tomography volume dose index, AAPM-TG 111, Radiation dosimetry https://fbt.tums.ac.ir/index.php/fbt/article/view/901 https://fbt.tums.ac.ir/index.php/fbt/article/download/901/490

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 02 16 1127 1127 Somaye Jabari Department of Algorithms and Computation, Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran Amin Ghodousian Tehran University Reza Lashgari Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran Babak A. Ardekani Center for Advanced Brain Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA 2024 10 14 2025 01 07 Purpose: This topic focuses on a comprehensive evaluation of various diffusion tensor imaging (DTI) estimation methods, such as linear least squares (LLS), weighted linear least squares (WLLS), iterative re-weighted linear least squares (IRLLS) and non-linear least squares (NLS). The article will explore how each method performs in terms of accuracy, efficiency in estimating the diffusion tensor and robustness against noise. Materials and Methods:  The study compares the methods using simulated diffusion-weighted MRI data. Time complexity and performance were evaluated across key metrics such as TRMSE, RMSE, MSD and ΔSNR. Results: The results of the study demonstrate that LLS and IRLLS consistently outperform other methods in terms of TRMSE, MSD and SNR, particularly in high-noise scenarios. NLS performs best in reducing RMSE but high noise causes it to fit to noise, so it is not robust. WLLS showed the weakest performance across all metrics. Conclusion: LLS and IRLLS provide a balance between accuracy and computational efficiency, making them practical for use in DTI analysis. https://fbt.tums.ac.ir/index.php/fbt/article/view/1127 https://fbt.tums.ac.ir/index.php/fbt/article/download/1127/493

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 03 04 979 979 Kumar Prasun PadmaKanya Multiple Campus Santosh Kumar Sharma Nepal College of Information Technology, Kathmandu, Nepal 2024 03 23 2025 02 24 Purpose: One of the increasing neurological disorders is Alzheimer's, which progressively weakens brain cells and leads to critical cerebral impairments like memory loss. The present diagnostic techniques comprise PET scans, MRI scans, CSF biomarkers, and others that frequently need manual power and time-consuming process which might not offer appropriate results. This emphasizes the requirement for more precise and potential diagnostic solutions. Materials and Methods: The proposed model utilizes AI-based Deep Learning (DL) techniques for effective multi-class classification of AD such as Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), Mild Cognitive Impairment (MCI), Cognitive Normal (CN) and Alzheimer’s Disease (AD) using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The proposed study utilizes Tri Branch Attention Network (TBAN) with Unified Component Incorporation (UCI) by capturing both spatial and channel attention information, by replacing the Squeeze and Excitation (SE) component in the conventional EfficientNet model and helps in addressing the concerns associated to imbalanced spatial feature distribution in images. Further, the incorporation of the proposed TBAN module in the Conv Layer helps, not only in terms of capturing the long-term dependence between the different channels of the network but also helps in retaining the specific location information to enhance the performance of the model. Similarly, the proposed UCI which is used in the MBConv layer deals with regularization, as the accuracy of the model can be dropped due to unbalanced regularization, hence the incorporation of UCI advocates strong regularization for combatting the concerns associated with overfitting and aids in providing better accuracy. Results: Eventually, the proposed framework is evaluated with different metrics and the accuracy value obtained by the proposed model is 0.95. Likewise, precision, recall, and F1 scores gained by the proposed work are 0.95, 0.95, and 0.95. Conclusion: The proposed research resolves significant gaps in the present diagnostic practices by implementing emerged AI techniques to improve the efficacy and accuracy of Alzheimer's diagnosis by medical imaging. Through enhancing the abilities of early detection, this proposed model holds the prospective to majorly affect treatment tactics for people affected with Alzheimer's. Finally, it led to better patient consequences and life quality. https://fbt.tums.ac.ir/index.php/fbt/article/view/979 https://fbt.tums.ac.ir/index.php/fbt/article/download/979/495

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 05 13 1091 1091 Naghmeh Firouzi Department of Medical Physics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. Ali Asghar Parach IRCM - Institut de Recherche en Cancérologie de Montpellier, INSERM U1194 – ICM, France. Kaveh Tanha Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran. Mohammad Rostami Faculty of Psychology, Tarbiat Modarres University, Tehran, Iran Parham Geramifar Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran 2024 08 22 2025 02 04 Purpose In epilepsy pre-surgical evaluations, semi-automated quantitative analysis of 18F-FDG brain PET images is a valuable adjunct to visual assessment for localizing seizure onset zones. This study investigates how adjusting image reconstruction parameters can enhance the accuracy of these quantitative results. Materials and Methods A total of 234 reconstruction parameters were applied to 18F-FDG brain PET images of a focal epilepsy patient. The parameters encompassed the 3D-Ordered-Subset Expectation Maximization image reconstruction method with resolution recovery (HD) and without (non-HD), various numbers of iterations and subsets (#it×sub), pixel sizes, and Gaussian filters. The accuracy errors were determined using the relative difference percentage (RD%) in measured SUVmax and the absolute Z-scores compared to reference values derived from the normal database reconstruction set serving as the benchmark. Results The study revealed that reconstructed images with 5mm or 8mm Full width at half maximum (FWHM) Gaussian filters yielded RD% values above 5% for SUVmax and Z-scores, indicating potential inaccuracy with higher values of post-smoothing filters. The recommended reconstruction sets with RD% values below 5% for both HD and non-HD images were those with a 3mm FWHM Gaussian filter and higher (#it×sub), specifically (5×21, 8×21), (5×21, 6×21), and (7×21, 8×21) for pixel sizes of 1.01 mm, 1.35 mm, and 2.03 mm, respectively. Conclusions The findings underscore the significant impact of altering the image reconstruction sets on the SUVmax and Z-scores. Furthermore, the inconsistent fluctuations of Z-scores emphasize the importance of using standard image reconstruction sets to ensure accurate and reliable quantitative outcomes in epilepsy pre-surgical evaluations. https://fbt.tums.ac.ir/index.php/fbt/article/view/1091 https://fbt.tums.ac.ir/index.php/fbt/article/download/1091/502

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 06 02 732 732 Mahya Badrasa Department of Medical Physics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Mojan Mobaraki Department of Medical Physics, Faculty of Medicine, International Campus, Shahid Sadoughi University of Medical Sciences and Health Services, Yazd, Iran Abolfazl Nickfarjam Department of Medical Physics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Mohammad Hasan Larizadeh Department of Radiation Oncology, Afzalipour Radiation Oncology Center, Kerman University of Medical Sciences, Kerman, Iran Nasim Namiranian Yazd Diabetes Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Nima Hamzian Department of Medical Physics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran 2023 06 15 2025 03 01 Purpose: The posterior oblique beams are increasingly common in radiotherapy techniques. The radiation beams traversing through the treatment couch would be attenuated and cause under-dosage in the tumor region. The attenuation of an IGRT carbon fiber Couch for different angles, energies, field sizes, measurement points, couch regions, and the ability of the Eclipse treatment planning system in dose prediction was investigated.   Materials and Methods: Vital Beam linear accelerator and Exact IGRT couch top from Varian were applied. At first, the couch coefficient was used to find the most attenuation angle. Then, at the most attenuation gantry angle, the attenuation measurements were performed in three measurement points of an inhomogeneous thoracic phantom using a farmer ionization chamber for three energies with six field sizes in three regions of an IGRT couch.   Results: In three regions of the IGRT couch and the angle of 130˚, the photon beam was most attenuated. The most significant difference between calculated and measured point doses was 1.855%.   Conclusion: The IGRT treatment couch in posterior oblique gantry angles decreased the dose in the measurement points due to gantry angle, field size, energy, and couch region. The Eclipse treatment planning system can sufficiently predict the tumor dose distribution. https://fbt.tums.ac.ir/index.php/fbt/article/view/732 https://fbt.tums.ac.ir/index.php/fbt/article/download/732/506

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 06 04 1160 1160 EN Mohammad Enferadi-aliabad Department of Medical Physics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Mohammad Broomand Department of Radiotherapy, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Abolfazl Nikfarjam Department of Medical Physics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran 2024 11 30 2025 02 11 Introduction Laryngeal cancer is a critical health issue, often treated using advanced radiation therapy techniques such as Intensity-Modulated Radiation Therapy (IMRT). The gamma index is a widely used metric for quality assurance in radiotherapy, assessing the agreement between planned and delivered dose distributions. Objective This study aims to evaluate the feasibility and accuracy of laryngeal IMRT treatment plans using three gamma analysis algorithms and varying evaluation parameters, including dose difference (DD%), distance-to-agreement (DTA).   Result Gamma passing rates (GPR) for the laryngeal IMRT plans demonstrated high accuracy, with over 90% of pixels passing the criteria in most cases. Composite gamma analysis showed 53.89% of pixels meeting both DD and DTA criteria simultaneously, while individual evaluation revealed the impact of stricter thresholds on GPR. Subtraction analysis identified dose discrepancies, emphasizing the need for accurate calibration. Conclusion This study highlights the effectiveness of gamma analysis in ensuring the accuracy of IMRT treatment plans for laryngeal cancer. The findings underscore the importance of rigorous PSQA, parameter optimization, and advanced algorithms to enhance treatment precision. https://fbt.tums.ac.ir/index.php/fbt/article/view/1160 https://fbt.tums.ac.ir/index.php/fbt/article/download/1160/507

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 07 24 1015 1015 EN Hadis Keshavarz Cardiovascular Diseases Research Center, Department of Cardiology, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran Hossein Sadr Department of Health Informatics and Intelligent system, Guilan Road Trauma Research Center, Trauma institute, Guilan University of Medical Sciences, Rasht, Iran Mojdeh Nazari Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Arsalan Salari Department of Cardiology, Cardiovascular Diseases Research Center, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran 2024 05 21 2025 04 09 Purpose: In the era of digital medicine, medical imaging serves as a widespread technique for early disease detection, with a substantial volume of images being generated and stored daily in electronic patient records. X-ray angiography imaging is a standard and one of the most common methods for rapidly diagnosing coronary artery diseases. Deep neural networks, leveraging abundant data, advanced algorithms, and powerful computational capabilities, prove highly effective in the analysis and interpretation of images. In this context, Object detection methods have become a promising approach. Materials and Methods: Deep learning-based object detection models, namely RetinaNet and EfficientDet D3 were utilized to precisely identify the location of coronary artery stenosis from X-ray angiography images. To this aim, data from about a hundred patients with confirmed one-vessel coronary artery disease who underwent coronary angiography at the Research Institute for Complex Problems of Cardiovascular Diseases in Kemerovo, Russia was utilized. Results: Based on the results of experiments, almost both models were able to accurately detect the location of stenosis. Accordingly, RetinaNet and EfficientDet D3 detected the location of false stenotic segments with a probability of more than 93% in the coronary artery. Conclusion: It can be stated that our proposed model enables automatic and real-time detection of stenosis locations, assisting in the crucial and sensitive decision-making process for healthcare professionals. https://fbt.tums.ac.ir/index.php/fbt/article/view/1015 https://fbt.tums.ac.ir/index.php/fbt/article/download/1015/514

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 08 12 1163 1163 Quang Trung PHAM 108 Military Central Hospital 2024 12 02 2025 02 20 Purpose: This study aims to explore the effect of mean dose constraint in optimization shells on the reduction of normal lung dose in lung SBRT plans. Materials and Methods: This study investigated 28 VMAT-based lung SBRT plans optimized with three artificial shells, which were re-generated with same setup and an additional mean dose constraint besides the maximum dose limit.  Dosimetric measurements of target volume and organs at risk (OARs) were compared between the original plans and re-generated ones using Wilcoxon signed-rank test at 5% level significance (two-tailed). Results: Replanning resulted in slight improvements in some parameters, such as R50% and Gradient measure (GM) respectively reduced by 1.3% and 1.0% with p<0.05, but slight increases in others, such as D2cm and Maximum target dose. However, those increases were not statistically significant. The Conformity Index (CI) and V105% values remained largely unchanged after replanning. The parameters for dose deposited in normal lung tissue showed statistically significant reductions ranging from 1.0% to 1.7%. In addition, the mean dose to the spinal cord, esophagus, and skin were slightly reduced, but the mean dose to the heart showed a slight increase. Conclusion: The study found that adding mean dose constraints to optimization shells in lung SBRT plans can reduce normal lung dose while maintaining dose conformity to the target. However, there may be slight changes in some OARs such as the spinal cord, esophagus, and skin. These changes were not statistically significant.   https://fbt.tums.ac.ir/index.php/fbt/article/view/1163 https://fbt.tums.ac.ir/index.php/fbt/article/download/1163/518

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 09 27 1174 1174 Yunus Soleymani Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran Amin Akbari Ahangar Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran Ata Pourabbasi Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran 2024 12 12 2025 02 12 Purpose: There is a known decline in brain volume with age, impacting cognitive health and increasing the risk of diseases such as dementia and Alzheimer's. Physical activity has been shown to have positive effects on brain structure and cognitive function with aging. Still, the association between motor function and brain volume in young adults remains unclear. Materials and Methods: This study utilized high-resolution T1-weighted MRI images and motor function test results from 1082 healthy young adults aged 22-37, sourced from the Human Connectome Project Young Adult (HCP-YA). Motor functions were assessed using four tests: Endurance, Gait Speed, Dexterity, and Strength. Correlation analysis and multiple linear regression models were used to evaluate the association between motor functions and brain volumes, adjusting for demographic variables and body mass index (BMI). Results: Significant positive correlations were found between Endurance and Strength tests with multiple brain volumes, while Dexterity test showed negative correlations. No significant correlations were observed for the Gait Speed test. Multiple linear regression analyses revealed that total brain (β = 0.045, SE = 0.020), total gray matter (GM) (β = 0.035, SE = 0.016), left white matter (WM) (β = 0.058, SE = 0.025), right WM (β = 0.056, SE = 0.025), total WM (β = 0.057, SE = 0.025), and left accumbens (β = -0.072, SE = 0.031) volumes were significantly associated with motor function scores (p < 0.05). Conclusion: Physical fitness, as measured by motor function tests, is significantly associated with brain structural integrity in young adults. These findings highlight the potential importance of physical activity in maintaining brain health, which could inform strategies to promote active lifestyles and prevent neurodegenerative diseases. https://fbt.tums.ac.ir/index.php/fbt/article/view/1174 https://fbt.tums.ac.ir/index.php/fbt/article/download/1174/527

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 09 09 956 956 Mustafa Mohammed Al-Tememi Aesthetic and Restorative Dentistry, College of Dentistry, University of Baghdad, Baghdad, Iraq Hussain Faisal Al-Huwaizi Aesthetic and Restorative Dentistry Department, College of Dentistry, University of Baghdad, Baghdad, Iraq. 2024 02 29 2024 04 15 Purpose: To evaluate the antibacterial efficacy of different concentrations of natural cold-pressed flaxseed oil when used as an intra-canal medicament against Enterococcus faecalis. Materials and methods: The antibacterial efficiency of flaxseed oil against E. faecalis was assessed in two sections using different concentrations. Both sections were compared to calcium hydroxide and tricresol formalin. The first section was on the agar, using two methods: agar diffusion and vaporization. The second section is on the extracted roots contaminated with E. faecalis for 21 days to form biofilms, confirmed by SEM examination, and includes two different methods: direct contact and vaporization. Bacterial swabs were collected before and after medication throughout two-time periods (3 and 7 days). The canal contents were swabbed using paper points kept for 1 minute in the root canal, and the collected samples were diluted and cultivated on plates containing blood agar. Survival fractions were determined by calculating the number of colony-forming units on culture medium after 24 hours. The oil's minimum inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) against E. faecalis were determined using the micro-broth dilution method. The active components in flaxseed oil were evaluated using GC-MS and HPLC analysis. Results: The tested oil demonstrated antibacterial efficacy against E. faecalis in different concentrations and levels. The MBC was 22.5 µl/ml. Tricresol formalin induced powerful antibacterial action, while calcium hydroxide exhibited less effective antibacterial action as compared to flaxseed oil. Flaxseed oil contains numerous biologically active components. Conclusion: Flaxseed oil exhibits strong antibacterial activity when evaluated against E. faecalis biofilm that has been cultivated in root canals. https://fbt.tums.ac.ir/index.php/fbt/article/view/956 https://fbt.tums.ac.ir/index.php/fbt/article/download/956/441

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 01 11 989 989 EN Khadija Masood The University of Lahore S Muhammad Yousaf Farooq The University of Lahore Hamnah Fatima University Institute of radiological sciences and medical imaging technology, faculty of Allied health sciences, The University of Lahore. Syeda Masooma Raza Naqvi University Institute of radiological sciences and medical imaging technology, faculty of Allied health sciences, The University of Lahore. Mahrukh Amna University Institute of radiological sciences and medical imaging technology, faculty of Allied health sciences, The University of Lahore. Amna khushi University Institute of radiological sciences and medical imaging technology, faculty of Allied health sciences, The University of Lahore. Rubiqa Muhammad Riaz University Institute of radiological sciences and medical imaging technology, faculty of Allied health sciences, The University of Lahore. Rana Saqib Javed University Institute of radiological sciences and medical imaging technology, faculty of Allied health sciences, The University of Lahore. 2024 04 04 2024 12 30 Background: Patients with diabetes are more likely to develop polyhydramnios. The rate of Polyhydramnios among diabetic patients is ascending when contrasted with non-diabetic patients. Objective: To compare the amniotic fluid index of diabetics and non-diabetics using sonography. Methods: 200 people participated in a case-control study, 100 of whom were diabetic and the other 100 were non-diabetic. Toshiba XARIO XG was used in the study at the university ultrasound clinic in Green Town. It has a convex probe of 3.5-7.5 MHz frequency. All patients with diabetes and gestational diabetes of age 18-45 years are included during 2nd & 3rd trimesters. Any underlying pathologies like hypertension, multiple gestations were excluded in this study. SPSS version 25.0 was utilized for the analysis of the data.  Results: The mean amniotic fluid index in diabetics and non-diabetics was 21.19 and 13.20 respectively.  In both diabetics and non-diabetics, the amniotic fluid index was found to be statistically significant (p=0.000). The chi-square analysis shows a significant association between AFI category and diabetes status. With the Diabetic group having a higher proportion of cases with Polyhydramnios AFI category and a lower proportion of cases with Normal AFI category compared to the Non-diabetic group. The mean estimated fetal weight in diabetics and non-diabetics was 1341.64 and 1372.53 respectively. Result shows that there was no significant difference in the estimated weight of the fetus between diabetic and non-diabetic females (p=0.088). Conclusions: Study concluded that diabetes during pregnancy is associated with a significant increase in amniotic fluid levels, leading to a higher likelihood of polyhydramnios. https://fbt.tums.ac.ir/index.php/fbt/article/view/989 https://fbt.tums.ac.ir/index.php/fbt/article/download/989/476

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 05 10 983 983 Dolley Srivastava Maharishi University of Information Technology,Lucknow Himanshu Pandey Department of Computer Science and Engineering,Faculty of Engineering and Technology, University of Lucknow, Lucknow, India Ambuj Agarwal Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India Richa Sharma Department of Computer Science, Maharishi University of Information Technology, Lucknow, India 2024 04 02 2025 04 08 In the past, heavy drinking was often linked to fatty liver. The prevalence of non-alcoholic fatty liver disease (NAFLD), which affects people who do not consume alcohol, has garnered a lot of attention in the last 20 years. Nearly all fatty liver diseases are now the leading cause of liver disease in industrialized nations. Fatty liver has traditionally been defined as having a hepatic fat content of more than 5% of liver weight. Several medical issues, including those caused by medications, poor diet, and infections, may lead to fatty infiltration of the liver. Modern scientific understanding, however, attributes fatty liver in most individuals to either being overweight or obese or to drinking too much alcohol. This research proposes a stacked ensemble approach to detect NAFLD efficiently and achieves 95.9% correct classification accuracy. It also compares the proposed method with other basic and boosting machine learning approaches. To improve machine learning for trustworthy and reliable NAFLD screening and diagnosis, we apply explainable AI methods to the ensemble model to identify the most influential features and patterns for NAFLD predictions. https://fbt.tums.ac.ir/index.php/fbt/article/view/983 https://fbt.tums.ac.ir/index.php/fbt/article/download/983/500

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 10 28 1258 1258 Hanie Arabian Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. Alireza Karimian Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran Hamid Reza Marateb Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. Carolina Migliorelli Unit of Digital Health, Eurecat, Centre Tecnològic de Catalunya, 08005 Barcelona, Spain Miquel Mañanas Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, BarcelonaTech (UPC), Barcelona, Spain Sergio Romero Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, BarcelonaTech (UPC), Barcelona, Spain Antonio Russi Epilepsy Unit, Hospital Quirón Teknon Rafał Nowak Magnetoencephalography Unit, Hospital Quirón Teknon, Barcelona, Spain 2025 04 14 2025 08 18 Purpose: Magnetoencephalography (MEG) is a brain imaging method with a high temporal-spatial resolution by recording neural magnetic fields. The data quality of this imaging method is reduced for reasons such as the failure of one or more sensors. This study aims to explore the efficiency of the various data reconstruction techniques in magnetoencephalography for the retrieval of poor-quality channels. Materials and Methods: We compared three surface reconstruction methods (Mean, Median, and Trimmed mean), two partial differential equations (modified Poisson and Diffusion equation), and a Finite Element-based interpolation method using data from 11 young adults (aged 30±12). Each technique was assessed in terms of time taken for reconstruction, R-squared, root mean squared error (RMSE), and signal-to-noise ratio (SNR) compared to a reference signal. Statistical tests (P-value < 0.05) were used to analyze the relationships between the mentioned evaluation criteria. Generalized Linear Models revealed that surface reconstruction methods and finite-element interpolation outperformed partial differential equations. Results: The Trimmed mean method achieved the highest R-squared (0.882 ± 0.0610) and lowest RMSE (0.0155 ± 0.00904) with a reconstruction time of 9.5154 microseconds for a 500 milliseconds epoch of a magnetoencephalography channel data. Conclusion: The surface reconstruction methods can recover the noisy or lost signal in magnetoencephalography with a suitable error and required time. https://fbt.tums.ac.ir/index.php/fbt/article/view/1258 https://fbt.tums.ac.ir/index.php/fbt/article/download/1258/535

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 10 28 1110 1110 Hanie Arabian Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. Alireza Karimian Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. Reza Rasti Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. Hossein Arabi Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland 2024 09 23 2025 04 08 Purpose: The hippocampus is a crucial brain region responsible for memory, spatial navigation, and emotion regulation. Precise hippocampus segmentation from Magnetic Resonance Imaging (MRI) scans is vital in diagnosing various neurological disorders. Traditional segmentation methods face challenges due to the hippocampus's complex structure, leading to the adoption of deep learning algorithms. This study compares four deep learning frameworks to segment hippocampal parts, including concurrent, separated, ordinal, and attention-based strategies. Materials and Methods: This research utilized 3D T1-weighted MR images with manually delineated hippocampus head and body labels from 260 participants. The images were randomly split into five folds for experimentation, each time one of those designated as the test set and the rest as the training set. Results: The findings indicate that both the concurrent and separated frameworks perform better than the ordinal and attention-based frameworks regarding the Dice and Jaccard coefficients. In head segmentation, the separated framework had a Dice similarity of 0.8748, a Jaccard similarity of 0.7794, and a Hausdorff distance of 5.4160. In body segmentation, the concurrent framework had a Dice similarity of 0.8616, a Jaccard similarity of 0.7591, and a sensitivity of 0.8437. Statistical results from the one-way ANOVA test showed a significant difference in performance for the body part (P-value=0.008), but not for the head region (P-value=0.652) between concurrent and separated frameworks. Comparing the concurrent with ordinal and attention-based frameworks showed a significant difference in both body and head regions (P-value<0.001 for both comparisons). Conclusion: Researchers must consider the differences between various frameworks while selecting a segmentation method for their specific task. Understanding the strengths and weaknesses of every framework is essential for deciding on the top-rated segmentation approach for precise applications. https://fbt.tums.ac.ir/index.php/fbt/article/view/1110 https://fbt.tums.ac.ir/index.php/fbt/article/download/1110/536

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 01 04 1165 1165 Akbar Farhadi Istanbul Technical University, Energy Institute, Division of Nuclear research, Istanbul, Turkiye Farhad Zolfagharpour Department of Physics, Faculty of Science, University of Mohaghegh Ardabili, Ardabil, Iran Ahmet Bozkurt Istanbul Technical University, Informatics Institute, Division of Computational Science and Engineering, Istanbul, Turkiye Arash Abdolmaleki Department of Biophysics, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran Asadollah Asadid Department of Biology, Faculty of Science, University of Mohaghegh Ardabili, Ardabil, Iran 2024 12 03 2025 02 15 Purpose: This article investigates the influence of testicular positioning and surrounding organ compositions on the absorbed dose in the testicles across a wide range of photon energies. Materials and Methods: Using the Digimouse phantom in Geant4 with the mesh approach, the absorbed dose and deposited energy in mouse testicular tissue were calculated. Organ compositions followed ICRP Publication 145 guidelines. Four identical mono-energetic planar radiation sources (10 × 2.2 cm) emitting photons in the 2–10,000 keV range were positioned equidistantly around the mouse phantom at the head, tail, and both sides, 2 cm away, to ensure uniform irradiation. Simulations were conducted both with surrounding organs in anatomically accurate positions and with these organs replaced by air to assess their impact on dose distribution. Results: Without surrounding organs, the absorbed dose was minimally influenced (<6%) by radiation source orientation. When surrounding organs were included, significant differences were observed, particularly at low photon energies (<25 keV), where notable radiation shielding occurred. Above 25 keV, adjacent organs increased energy deposition in testicular tissue due to secondary scattering, with absorbed dose differences between opposing orientations (e.g., head vs. tail) ranging from 30–92%. At 25 keV, surrounding organs did not affect energy deposition. Conclusion: Surrounding organs significantly influence testicular absorbed dose, particularly at low photon energies where shielding dominates, and at higher energies where secondary scattering enhances deposition. These findings highlight the importance of considering organ interactions and source positioning in dosimetry to optimize radiation therapy protocols and reduce risks to sensitive organs. https://fbt.tums.ac.ir/index.php/fbt/article/view/1165 https://fbt.tums.ac.ir/index.php/fbt/article/download/1165/537

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 01 04 1137 1137 Hanieh Alimiri Dehbaghi kermanshah university of medical sciences Karim Khoshgard kermanshah university of medical sciences Samira Jafari Khairabadi kermanshah university of medical sciences 2024 10 31 2025 06 02 Objective: The initial evaluation of trauma poses a formidable and time-intensive challenge. This study aims to scrutinize the diagnostic efficacy and utility of integrating machine learning models with radiomics features for the identification of blunt traumatic kidney injuries in abdominal CT images. Methods: This investigation involved the collection of 600 CT scan images encompassing individuals with varying degrees of kidney damage resulting from trauma, as well as images from healthy subjects, sourced from the Kaggle dataset. An experienced radiologist performed the segmentation of axial images, and radiomics features were subsequently extracted from each region of interest. Initially, 30 machine learning models were deployed, with a final selection narrowed down to three models: Light Gradient-Boosting Machine (LGBM), Ridge Classifier, and Adaptive Boosting (AdaBoost). The performance of these chosen models was subjected to a more comprehensive examination. Results: The AdaBoost model exhibited notable performance in diagnosing mild kidney injury, achieving accuracy and sensitivity rates of 93% and 94%, respectively. Furthermore, for severe kidney injury, the AdaBoost model demonstrated a remarkable sensitivity of 96% and an accuracy of 97%. The Area Under the Curve (AUC) values for this model were also calculated, yielding values of 92.91% and 97.04% for mild and severe renal injuries, respectively. Conclusion: The artificial intelligence models employed in this study hold significant potential to enhance patient care by providing valuable assistance to radiologists and other medical professionals in the diagnosis and staging of trauma-related kidney injuries. These models offer the capability to prioritize positive studies, expedite evaluations, and accurately identify more severe injuries that may necessitate immediate intervention. Of course, in this study, the compatibility of artificial intelligence tools with the clinical environment has not been discussed, and only the ability of machine learning models to interpret CT scan images has been investigated. https://fbt.tums.ac.ir/index.php/fbt/article/view/1137 https://fbt.tums.ac.ir/index.php/fbt/article/download/1137/538

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 01 06 606 606 Mahsan Hajihosseini a Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran Omid Asadi a Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran Sima Shirzadi a Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran Zahra Einalou Massachusetts General Hospital and Harvard Medical School, Optics at Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States Mehrdad Dadgostar Massachusetts General Hospital and Harvard Medical School, Optics at Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States 2022 11 18 2024 01 19 Using functional near-infrared spectroscopy (fNIRS) as a complementary and cost-effective neuroimaging technique in sensorimotor tasks due to its applications in brain-computer interface (BCI) research can provide useful information about functional connectivity of brain networks. However, few studies on brain functional connectivity during sensorimotor tasks have often focused on evaluating brain activity electrically. In the present study, a signal processing algorithm using fNIRS-HbO2 data has been suggested to find active parts of the brain for motion and motor imagery in motor imagery task. In this algorithm, first, the wavelet transform was used to remove the noise and preprocess the signal. Then, using correlation analysis, functional connectivity matrices in motion and motor imagery were extracted, and finally, global efficiency ​​(GE) values were calculated. In addition to investigating the conditions of the small-world network in the connectivity matrix, the classification of motion and motor imagery was investigated using a t-test. For this purpose, a 20-channel fNIRS signal was recorded to measure changes in HbO2 concentration in the motor cortex of 12 healthy individuals with a sampling frequency of 10 Hz. The results, in addition to confirming the presence of a small-world network in the graphs from the correlation matrix, showed that the classification of motion and motor imagery of right and left hands will be significant when 40% of the strongest connectivity between channels was selected. The results showed that in the left hemisphere there was stronger connectivity between the channels. In general, the results not only showed the activity of brain networks in performing sensorimotor tasks as small-world networks, but they also reported the role of the dominant hemisphere in performing these tasks. https://fbt.tums.ac.ir/index.php/fbt/article/view/606 https://fbt.tums.ac.ir/index.php/fbt/article/download/606/539

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 02 15 1507 1507 Masomeh Najafabadi Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. Soheil Elmtalab Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. Arman Esmailzadeh Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. Mostafa Alizade-Harakiyan Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran Hamed Zamani d Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran. Ahmad Shanei Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. Nadia Najafizadeh Department of Radiation Oncology, School of Medicine, Seyyed Al-Shohada Hospital, Isfahan University of Medical Sciences, Isfahan, Iran. Ali Yousefi Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran. 2025 10 29 2026 02 07 Purpose: This study compares helical Tomotherapy in the supine position with three-dimensional Conformal Radiotherapy (3D-CRT) delivered in both prone and supine positions in pendulous left-breast cancer patients, aiming to achieve optimal target coverage while minimizing dose to Organs At Risk (OARs). Materials and Methods: Twenty non-metastatic patients with large pendulous left breasts received three separate treatment plans (3D-prone, 3D-supine, and Tomo-supine). Dose–Volume Histogram (DVH)-based indices, including Dmean, V5Gy, V10Gy, V20Gy, and V30Gy for the heart, ipsilateral lung, contralateral lung, and contralateral breast, as well as CI and HI for the PTV, were evaluated. Plans were generated using TIGRT (3D-CRT) and Accuray Precision® Tomotherapy TPS. Statistical analysis was performed using paired t-tests, and p < 0.05 was considered significant. Results: Tomotherapy achieved superior target conformity and homogeneity (higher CI and lower HI) and slightly increased PTV Dmean compared to 3D-CRT. However, it increased low- and intermediate-dose exposure to the ipsilateral lung and contralateral organs while significantly reducing high-dose volumes. The prone 3D-CRT approach demonstrated the lowest ipsilateral lung mean dose among all techniques. Conclusion: While tomotherapy provides excellent PTV coverage and dose uniformity, it increases low-dose exposure to OARs compared with 3D-CRT. Therefore, the choice of technique should be individualized based on clinical priorities, particularly in younger patients, where secondary cancer risk may be of concern. https://fbt.tums.ac.ir/index.php/fbt/article/view/1507 https://fbt.tums.ac.ir/index.php/fbt/article/download/1507/553

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 02 17 1554 1554 Hadi Rezaei Department of Radiology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran Babak Mahmoudian Medical Radiation Sciences Research Center, Tabriz University of Medical Sciences, Iran Asra Sadat Talebi Medical Radiation Sciences Research Center, Tabriz University of Medical Sciences, Iran 2025 12 16 2025 12 26 Purpose: Beta-emitting radionuclides used in liver cancer therapy generate cytotoxic effects and Cherenkov radiation. This study aims to evaluate the potential of Cherenkov radiation to induce localized hyperthermia in hepatic tumors during radionuclide therapy. Materials and Methods: Monte Carlo simulations were performed using the GATE platform to model Cherenkov radiation transport and heat deposition in hepatic tumor tissue. The absorbed thermal dose was quantified using the integrated bioheat transfer model, allowing accurate voxel-level mapping of temperature distribution. Six beta-emitting radionuclides, including ³²P, ⁹⁰Y, ¹⁶⁶Ho, ¹⁸⁸Re, ¹⁷⁷Lu, and ¹³¹I, were evaluated to assess their potential for inducing thermal effects through Cherenkov radiation absorption during liver radionuclide therapy. Results: Among the radionuclides studied, ³²P and ⁹⁰Y generated the highest number of Cherenkov photons in the liver tumor, resulting in significant heat deposition and uniform tumor temperatures ranging from 41 to 49°C, consistent with mild hyperthermia and, in the case of ³²P, partial thermal ablation with approximately 20% of the tumor volume exceeding 60°C. ¹⁶⁶Ho induced moderate heating, raising tumor temperatures to around 41°C in most of the tumor volume. In contrast, radionuclides with lower beta energies, such as ¹⁷⁷Lu, ¹³¹I, and ¹⁸⁸Re, produced minimal Cherenkov photon emission, resulting in negligible thermal effects within the tumor. Conclusion: The integration of radionuclide therapy with Cherenkov radiation-induced hyperthermia presents a promising strategy for radiosensitization and thermal ablation in hepatocellular carcinoma. https://fbt.tums.ac.ir/index.php/fbt/article/view/1554 https://fbt.tums.ac.ir/index.php/fbt/article/download/1554/555

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 02 26 1177 1177 Subba Rao Dusari VIT-AP university Dr. Nagendra Panini Challa VIT-AP University 2024 12 13 2025 05 24 Purpose: Lung cancer is the most common and deadly type of disease that is the cause of one million deaths around the world every year. Due to the present level of medical research, identifying lung tumors on chest computed tomography (CT) images have become a significant process in modern medicine. Enhancing treatment and reducing lung cancer mortality can be achieved by promptly identifying and accurately diagnosing suspected malignant lung tumors. While many deep learning algorithms have been developed recently for the classification of lung cancer, getting high accuracy in lung cancer classification is still a challenge. An advanced deep learning technique is developed to boost the effectiveness of early lung cancer diagnosis. Materials and Methods: In this research, we have proposed an enhanced ensemble deep-learning model for lung cancer classification and segmentation. Initially, we carried out an extensive preprocessing process including image resizing, noise reduction, and contrast enhancement to enhance the image quality. The problem of small sample size is addressed by applying conventional data augmentation techniques like flipping, rotating, zooming, and shearing. Next, six statistical features are retrieved using Improved Empirical Wavelet Transforms (IEWT). After feature extraction, the Enhanced ResNeXt model is used to classify lung cancer into normal, malignant, and benign classes. The interested region of the lung tumor is segmented using the Modified ShuffleNetV2 model. Depending on whether lung cancer is present or not, individuals with lung cancer are classified as normal, malignant, and benign and the experiments are performed on the benchmark datasets LIDC-IDRI and IQ-OTH/NCCD. Results: The proposed approach achieves an exceptional model accuracy of 99.43% for the IQ-OTH/NCCD dataset and 99.37% for the LIDC-IDRI dataset. The expected outcomes show that the accuracy and efficiency of our proposed ensemble deep learning model outperform other CNNs. Conclusion: The proposed models beat existing CNN-based models in terms of speed and number of training parameters, which means that using CT scan images to diagnose lung cancer automatically is a suitable option and a strong selection for extensive use in medical environments. https://fbt.tums.ac.ir/index.php/fbt/article/view/1177 https://fbt.tums.ac.ir/index.php/fbt/article/download/1177/556

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 04 23 1329 1329 EN Shaghayegh Abroshan M.Sc in Medical Physics, Student Research Committee, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran. Farzaneh Allahveisi 0009-0002-4679-7839. Karim Khoshgard Department of Medical Physics, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran Bahar Moussas Ghaffari Department of Radiology, School of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran. Mohsen Bakhshandeh Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Siamak Derakhshan Department of Radiotherapy and Nuclear medicine, Faculty of Paramedical Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran. 2025 06 28 2025 10 03 Purpose: The main goal of radiotherapy is to deliver a lethal radiation dose to tumor tissue while minimizing the dose to healthy tissues. Treatment planning in radiotherapy requires precise determination of the treatment volume and specification of the radiation dose to both the tumor and healthy tissues. To define the treatment volume in radiotherapy, margins are considered around the tumor tissue, which may include a portion of normal tissue. Fusion of PET-CT images with CT images in the three-dimensional conformal radiotherapy for lung cancer may improve treatment by more accurately and precisely determining the Gross Tumor Volume (GTV). This study aimed to compare the treatment volumes and dosimetric parameters between conventional treatment planning (using CT images only) and treatment planning using PET-CT image fusion in 3D-conformal radiotherapy for lung cancer. Materials and Methods: All lung cancer patients who were referred to our Radiotherapy center over two years were examined. PET-CT images and simulation CT scans of 15 patients with non-metastatic lung cancer were analyzed. All patients were treated using the 3D-conformal radiotherapy method. The treatment planning and image fusion were performed using the ISOGRAY treatment planning software. The volumetric as well as dosimetric parameters, such as mean dose, maximum dose in the target volume, and other dose-volume parameters in organs at risk such as lung tissue including V5, V13, and V20 were compared between the two groups (including the conventional treatment planning group (only using CT data) and the treatment planning using the PET-CT image fusion group). Results: A total of 23 lung cancer patients were included during the study period; of these, eight patients were excluded due to having metastatic lung cancer. The variation in Gross Tumor Volume (GTV) among the different patients was significantly high. The fusion of PET images with CT scans increased the GTV in 11 patients (on average by 48±89.7%) and decreased it in 5 cases (46.4±98.0%). According to the results (considering all patients), no statistically significant difference was noted in the Contoured Tumor Volume (CTV) between the conventional method (based on CT images only) and the method based on the fused images with PET-CT images (P-value > 0.05). There was no statistically significant difference in maximum dose at healthy tissues/Organs At Risk (OARs), including the ipsilateral lung, contralateral lung, skin, and spinal cord, between treatment planning based on CT images and based on fusion with PET-CT data (P-value > 0.05). The mean dose in the lung (in involved side %43±.16.66) decreased (on average by %41±21.00) after fusion with PET-CT images (P-value > 0.05). Conclusion: The use of PET-CT data in radiotherapy treatment planning for lung cancer patients undergoing adaptive 3D radiotherapy can have an improving and impactful role. The fusion of PET-CT images with CT data had a significant effect on the tumor volume definition, resulting in changes in the gross tumor volume in most patients. It is recommended to utilize data from both anatomical (CT) and functional (PET) imaging modalities for a better assessment and definition of tumor volume, as each modality has its advantages and limitations. However, by combining them, the tumor volume can be determined with greater precision and accuracy. Additionally, improving the precision in defining tumor volume can reduce the radiation dose received by healthy tissues/organs at risk https://fbt.tums.ac.ir/index.php/fbt/article/view/1329 https://fbt.tums.ac.ir/index.php/fbt/article/download/1329/557

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 05 07 1573 1573 Firouz Amani Ardabil University of Medical Sciences Ali Tarighatnia Ardabil University of Medical Sciences Masoud Amanzadeh Department of Health Information Management, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran Shafagh Ali Asgarzadeh Ardabil University of Medical Sciences Sara Jalali Ardabil University of Medical Sciences Mahnaz Hamedan Ardabil University of Medical Sciences Nader Nader Department of Anesthesiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA 2026 01 01 2026 04 17 Purpose: Osteoporosis is a condition where bone density decreases, impacting bone quality and increasing susceptibility to fractures. Diagnosis typically involves imaging techniques like DEXA scan. To enhance early detection, new predictive algorithms are essential due to limitations in clinical diagnostic software. This study aims to predict osteoporosis with various machine learning (ML) algorithms and pinpoint factors contributing to the disease based on DEXA scans of the femoral neck and lumbar spine. Methods: In this study, we analyzed the data from 1000 people who were encountered for densitometry at the Rheumatology Clinic in a major metropolitan general hospital for predicting osteoporosis, we used five classification algorithms, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (LR). We evaluated the performance of models using four metrics, including accuracy, sensitivity, specificity, and Area Under the Receiver Operating characteristic Curve (AUROC). All of programing was done using the Python programming language in the Google Colab environment. Results: Out of 1000 patient records, there were 761 women and 239 men, with a 58.42 mean age. Osteoporosis occurred in 23.5% of cases. ANN and RF, with 89% and 78.6%, had the highest sensitivity, respectively. ANN and SVM, with 96.3% and 94.2%, had the highest specificity. In accuracy, ANN and RF, with 94.6% and 86.5%, were the highest. Based on the AUROC, the ANN method achieved the best performance (0.937), followed by RF (0.837), LR (0.832), SVM (0.769), and DT (0.715). Conclusion: The ANN model emerged as the strongest performer, achieving high sensitivity, specificity, and overall accuracy. The study’s findings hold promise for enhancing the earlier diagnosis of osteoporosis. Machine learning algorithms can provide an alternative approach to identifying and screening individuals at high risk for osteoporosis and can be used in the development of clinical decision support systems for the diagnosis of osteoporosis. https://fbt.tums.ac.ir/index.php/fbt/article/view/1573 https://fbt.tums.ac.ir/index.php/fbt/article/download/1573/558

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 05 1268 1268 Nazar Adnan Mohammed Department of Physiology and Medical Physics, College of Medicine, Al-Nahrain University, Baghdad, Iraq. Siham Abdullah 2. Department of Physiology and Medical Physics, College of Medicine, Al-Nahrain University, Baghdad, Iraq Sabbar Al-Bayaty Al-Amal national hospital for cancer treatment, Baghdad Medical City, Iraqi Ministry of Health, Baghdad, Iraq Mustafa Aldulaimy Department of Physiology, College of Medicine, University of Mosul, Mosul, Iraq Abdulrahman Abdulbaqi Al-Amal national hospital for cancer treatment, Baghdad Medical City, Iraqi Ministry of Health, Baghdad, Iraq Nabaa Mohammed Alazawy Ghazi Al-Harriri Hospital for Specialist Surgeries, Baghdad Medical Complex, Ministry of Health, Baghdad, Iraq 2025 04 29 2025 10 03 Background: A very effective treatment for lung cancer is radiation therapy. The goal of this study was to find a better way to treat lung cancer with radiation, and it did so by combining three different types of radiation: conventional radiation treatment, intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT). Patients and methods: Thirty people with a diagnosis of unilateral lung cancer participated in this study. Patients were treated using ELEKTA's linear accelerator with 6 MV or 10 MV energy x-ray photon beams. To plan their therapies, the patients underwent a CT simulation using MONACO version 5.1. Results: In the comparison of VMAT with IMRT, the former demonstrates superiority in several aspects, including enhanced protection of the heart and spinal cord, reduced segments and monitoring units, and more comprehensive coverage of the projected target volume (PTV). Conclusion: VMAT demonstrates superior results compared to IMRT in administering radiation to lung tumors while preserving the heart and spinal cord. https://fbt.tums.ac.ir/index.php/fbt/article/view/1268 https://fbt.tums.ac.ir/index.php/fbt/article/download/1268/560

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 10 1039 1039 Jasmine Christabel G Department of Computer Application, Noorul Islam Center for Higher Education, Kumaracoil, India A.C. Subhajini Department of Computer Application, Noorul Islam Center for Higher Education, Kumaracoil, India 2024 06 15 2025 04 06 Purpose: Diseases are unavoidable because of environmental factors, changes in diet, hereditary issues and many other factors; hence, it is important to detect diseases via various techniques in the healthcare sector to identify and diagnose the disease. Therefore, the proposed model focuses on employing advanced techniques for detecting heart disease, thyroid disease, and hepatitis, as these diseases have become common in recent years, along with the prediction of heart rate. Materials and Methods: The proposed work employs modified PCA (principal component analysis) for dimensionality reduction to extract appropriate features for the model by utilizing two learning rates (LR1 and L2). Furthermore, the modified back propagation (BP) method is used for effective classification of heart, thyroid, hepatitis, and heart rate prediction by incorporating adaptive Gaussian white noise (AWGN). In the proposed model, three different datasets are utilized: a heart disease dataset, a thyroid dataset, a hepatitis dataset for classification, and a heart rate prediction dataset for regression. Results: The accuracy, precision, recall, and F1 scores obtained by the proposed model for the heart disease dataset are 97.8%, 98%, 98%, and 98%, respectively. Similarly, 97.2%, 98%, 89%, and 93% for the thyroid dataset, respectively. Finally, the accuracy, precision, recall, and F1 score obtained by the proposed model for hepatitis are 95%, 98%, 88%, and 92%, respectively. Like the classification of diseases, heart rate prediction was also evaluated via different metrics, such as the RMSE, MSE, MAE, and R2. The MAE obtained by the proposed model for the heart rate prediction dataset is 0.112; likewise, the R2 obtained is 0.99, the MSE attained is 0.022, and the RMSE value obtained is 0.1488. Conclusion: The results of the proposed mechanism reflect its ability to detect different diseases effectively. This is due to the successful implementation of advanced AI approaches in the proposed framework. https://fbt.tums.ac.ir/index.php/fbt/article/view/1039 https://fbt.tums.ac.ir/index.php/fbt/article/download/1039/561

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 11 1311 1311 Sara Shomal-Nasab Arak University Hossein Sadeghi Arak University Fatemeh Seif Arak University of Medical sciences Mohammad Reza Bayatiani Arak University of Medical sciences 2025 06 04 2025 11 03 Background: Small electron fields are used in radiotherapy for superficial tumors and areas close to the skin. However, the impact of tissue heterogeneity on dose distribution in these fields poses considerable challenges. To explore how the variability of cold foam affects dose distribution in small electron fields using Semiflex 3D and Advanced Markus dosimeters. Methods: Dosimetric measurements were performed using an Elekta Vera-HD linear accelerator with 10 MeV and 12 MeV electron beams. Square field sizes of 2×2, 3×3, 4×4, 5×5, and 6×6 cm² were investigated. Dose distributions were assessed using Semiflex 3D and Advanced Markus ionization chambers. Percentage Depth Dose (PDD) curves were analyzed, revealing that at 10 MeV, the depth of maximum dose (d_max) was 2.2 cm, while at 12 MeV, it increased to 2.7 cm. Results: The results confirm that the OF increases with both field size and beam energy. Larger field sizes enhance lateral electron scattering, and higher beam energy enables deeper penetration and broader dose distribution, further increasing the OF. A minimum field size of 3 cm × 3 cm is recommended, as differences between dosimeters were observed in 2 cm × 2 cm and 3 cm × 3 cm fields, but remained below 2% for larger fields. The study also found that increasing heterogeneity reduces the OF, with air-equivalent heterogeneities consistently decreasing the OF across all field sizes and energy levels. Conclusion: Monte Carlo algorithms in treatment planning systems (TPS) model heterogeneities using CT images and Hounsfield Unit (HU) values. However, their accuracy depends on CT image quality and device calibration, and HU-to-parameter conversion may not fully account for tissue variability. Combining computational simulations with experimental validation is recommended to improve TPS accuracy, especially in electron mode and when dealing with heterogeneities. https://fbt.tums.ac.ir/index.php/fbt/article/view/1311 https://fbt.tums.ac.ir/index.php/fbt/article/download/1311/562

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 11 1390 1390 Safoura Ashoorisefat Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran Mohammad Pooyan Alia Saberi Neurology Department, Guilan University of Medical Sciences, Rasht, Iran 2025 08 05 2025 12 06 Migraine is a complex neurological disorder characterized by dynamic alterations in brain activity during multiple phases: interictal (baseline), preictal, ictal, and postictal. This study aims to model and differentiate these migraine phases using electroencephalogram (EEG) and a Hidden Markov Model (HMM). EEG signals were collected from each subject over several months through frequent, short sessions—often multiple times per day. The recordings were temporally aligned with self-reported symptom diaries, allowing for precise labeling of migraine phases. A comprehensive set of features was extracted from the EEG signals, including spectral, temporal, and nonlinear measures—such as Dynamic Mode Decomposition (DMD) and Katz Fractal Dimension (KFD)—across various frequency bands. Despite the limited number of participants, the dense long-term recordings captured multiple migraine episodes, enabling reliable phase modeling. The HMM identified distinguishable neural patterns corresponding to migraine states, suggesting the feasibility of temporal EEG modeling for clinical applications in personalized migraine management. https://fbt.tums.ac.ir/index.php/fbt/article/view/1390 https://fbt.tums.ac.ir/index.php/fbt/article/download/1390/568

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 11 1298 1298 Maryam Samani https://orcid.org/0000-0002-5702-7082 Ali Gharekhani - Parastoo Farnia https://orcid.org/0000-0002-7554-5545 Bahador Makki Abadi https://orcid.org/0000-0002-9775-4057 2025 05 27 2025 10 15 Purpose: This study aims to implement Capsule Networks for ultrasound beamforming and image reconstruction, addressing the limitations of conventional Convolutional Neural Networks (CNNs) in embedded systems. The goal is to enhance image quality from single-plane wave transmission using fewer parameters while maintaining diagnostic accuracy. Materials and Methods: We propose a novel image reconstruction architecture, UCGNet (U-Caps-GAN Network), which integrates Capsule Networks (U-Caps) within a Generative Adversarial Network (GAN) framework. The method is applied to reconstruct high-quality ultrasound images from single-plane wave data and is evaluated using the Plane-wave Imaging Challenge in Medical Ultrasound (PICMUS) dataset. Results: The reconstructed images achieved a mean signal-to-noise ratio (SNR) of 18.4383 and a peak signal-to-noise ratio (PSNR) of 41.0226, outperforming the baseline UNet model in terms of accuracy. Moreover, UCGNet used less than 25% of the training parameters compared to UNet. Conclusion: UCGNet provides an effective and lightweight solution for ultrasound image reconstruction. Its improved accuracy and reduced parameter count make it well-suited for practical medical imaging applications, particularly in resource-constrained environments. https://fbt.tums.ac.ir/index.php/fbt/article/view/1298 https://fbt.tums.ac.ir/index.php/fbt/article/download/1298/564

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 12 1247 1247 Mahvash Zargar Fertility, Infertility and Perinatology Research Center, Department of Obstetrics and Gynecology, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Seyed Masoud Rezaeijo Ahvaz Jundishapur University of Medical Sciences Mahin Najafian Fertility, Infertility and Perinatology Research Center, Department of Obstetrics and Gynecology, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Kobra Shojaei Fertility, Infertility and Perinatology Research Center, Department of Obstetrics and Gynecology, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Vahideh Yousefvand Fertility, Infertility and Perinatology Research Center, Department of Obstetrics and Gynecology, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 2025 03 27 2025 08 02 Introduction: Predicting the success of assisted reproductive technology (ART) remains a significant challenge due to the complex interplay of clinical, embryological, and demographic factors. This study aimed to develop and evaluate machine learning models, particularly deep learning-based approaches, to identify key predictors of ART success and improve outcome prediction accuracy.   Methods: A retrospective study was conducted on 500 infertile couples undergoing ART treatment between 2019 and 2024. A comprehensive dataset, including 84 clinical, embryological, and demographic variables, was analyzed. The key predictors included endometrial thickness, endometrial pattern, embryo transfer day, and hormonal markers (PRL, LH). Four machine learning models were implemented: Decision Tree, Random Forest, XGBoost, and a Transformer-Based Model. Data preprocessing involved feature selection, missing data handling, normalization, and oversampling techniques to address class imbalance. The models were trained and validated using k-fold cross-validation, and performance was assessed using accuracy, precision, recall, and F1 score.   Results: The Transformer-Based Model achieved the highest accuracy (99.7%), outperforming traditional machine learning models. Endometrial pattern (r = 0.69) and endometrial thickness (r = 0.82) were the strongest predictors of ART success, emphasizing the dominant role of uterine factors. While female age and infertility duration had a weak negative correlation, male infertility factors and lifestyle variables (smoking, alcohol consumption) showed minimal predictive significance. Model-based feature importance confirmed uterine and embryological factors as the primary determinants of ART success, suggesting a shift in treatment focus.   Conclusions: This study highlights the superiority of deep learning models in ART success prediction, with uterine factors emerging as the strongest predictors. Integrating AI-driven predictive models into clinical practice can enable personalized ART treatment, improved patient counseling, and optimized embryo transfer strategies, ultimately enhancing fertility outcomes. https://fbt.tums.ac.ir/index.php/fbt/article/view/1247 https://fbt.tums.ac.ir/index.php/fbt/article/download/1247/565

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 12 1185 1185 Hamideza Jalali Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran majid pouladian Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Ali Motie Nasrabadi Biomedical Engineering Department, Faculty of Engineering, Shahed University, Tehran, Iran Azin Movahed Music Department, School of Performing Arts and Music, College of Fine Arts, University of Tehran, Tehran, Iran 2024 12 31 2025 02 15 Purpose: Automatic classification of sleep stages is one of the fundamental factors in diagnosing sleep disorders to prevent and treat various diseases, and it can significantly aid in saving specialists' time and energy. In this study, a novel method for mapping electroencephalogram (EEG) signals to music for sleep stage classification is proposed. Materials and Methods: A total of 15.233, 30-second data segments from the Sleep-EDF database were used as the statistical population for this evaluation. Initially, single-channel EEG data are mapped to musical pieces using a long short-term memory (LSTM) network structure. Subsequently, seven features are extracted from the generated music sequences and applied to classification structures. Results: The overall classification accuracy for the five sleep stages according to the AASM standard is 85.3% for the Sleep-EDF database. Another objective of this study is to present a novel single-channel EEG sonification method, achieving classification accuracy that is either higher than or comparable to contemporary methods.  Conclusion: The results of this study show that this audio signal mapping contains effective information for sleep stage classification and the proposed method performs well compared to new methods without the need for complex classifier structures. https://fbt.tums.ac.ir/index.php/fbt/article/view/1185 https://fbt.tums.ac.ir/index.php/fbt/article/download/1185/566

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 16 1556 1556 EN Ahad Zeinali Department of Medical Physics, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran Zubaida Bakr Al jawali Department of Medical Physics, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran Rana Awny Department of Medical Physics, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran Omar Muayad Sultan Department of Radiology, College of Medicine, Tikrit University, Tikrit, Iraq Naser Rasouli Urmia University of Medical Sciences 2025 12 16 2026 02 23 Purpose : Computed tomography (CT) is an essential diagnostic imaging modality in pediatric medicine; however, exposure to ionizing radiation poses a potential risk of radiation-induced malignancy. This multicenter study evaluated organ-specific radiation doses and estimated lifetime cancer risks in pediatric patients undergoing chest and abdominal CT imaging across multiple Iraqi healthcare centers. Material & Methods: Data from 200 pediatric patients (100 chest CT, 100 abdominal CT) were collected from three hospitals in Dohuk, Mosul, Anbar, and Baghdad, Iraq. CT acquisition parameters (kilo-voltage (kVp), pitch, slice thickness, scan length) and dose metrics (CTDIvol, DLP, effective dose) were recorded. Organ doses to lungs, thyroid, breast, and heart were estimated using imPACT software based on scanner-reported parameters. Lifetime Attributable Risk of cancer incidence was calculated using BEIR VII risk models with linear interpolation for pediatric age groups (1–5, 6–10, 11–15 years). Results: Radiation dose metrics increased significantly with age. Mean CTDIvol ranged from 8.1 ± 1.1 mGy (1–5 years) to 20.8 ± 1.2 mGy (11–15 years), with effective doses of 3.2 ± 0.3 to 6.7 ± 0.5 mSv. Organ doses demonstrated parallel trends: lung doses 4.8–8.9 mSv, thyroid doses 4.3–8.0 mSv. Cancer risk was highest in younger children (LAR: 0.16 ± 0.02 cancers/10,000 person-years/mSv), decreasing with age but remaining clinically significant. Conclusions: This study demonstrates that as children age and body size increase, CT dose and cancer risk increase; therefore, dose optimization and the use of appropriate protocols are essential. https://fbt.tums.ac.ir/index.php/fbt/article/view/1556 https://fbt.tums.ac.ir/index.php/fbt/article/download/1556/567

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 16 1079 1079 EN Leila Golchin Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran. Maryam Noroozian Professor of neurology, Director; Cognitive Neurology, Dementia and Neuropsychiatry (CNNRC), Tehran University of Medical Sciences (TUMS), Tehran, Iran Seyed Amir Hossein Batouli Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran. Mohammad Ali Oghabian Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran. Research Center for Molecular and Cellular Imaging (RCMCI), Tehran, Iran, http://Rcmci.tums.ac.ir 2024 08 04 2025 01 08 Purpose: Primary Progressive Aphasia (PPA) is a neurodegenerative syndrome characterized by progressive language impairment. The present study investigated white matter (WM) microstructural changes in PPA patients and their relationship with language and neuropsychological functions. Materials and Methods: Diffusion tensor imaging (DTI) was used to examine 29 PPA patients and 13 healthy controls, focusing on 18 white matter tracts in both hemispheres. Results: Significant differences in diffusivity values were observed between PPA patients and controls in multiple tracts, including the Cingulum, Arcuate Fasciculus (AF), Superior Longitudinal Fasciculus (SLF), Inferior Fronto-Occipital Fasciculus (IFOF), Inferior Longitudinal Fasciculus (ILF) bilaterally, as well as the left Uncinate Fasciculus (UF). Correlations between WM integrity and language functions were found in both hemispheres, with the left Cingulum showing positive correlations with various language measures. Notably, right hemisphere tracts (IFOF, ILF, SLF) positively correlated with several language domains, suggesting a potential compensatory role. White matter microstructural changes also correlated with neuropsychological functions, highlighting PPA's interconnections of language and cognitive domains. Conclusion: To our knowledge, the present study is the first to identify specific correlations between right hemisphere tracts, language domains, and cognitive functions in PPA patients. Our findings contribute to understanding the neural basis of language impairment in PPA, emphasizing the bilateral nature of language processing in neurodegenerative disorders. The results have implications for diagnosis, prognosis, and treatment planning in PPA, suggesting the need for therapeutic approaches that consider both hemispheres and the interplay between language and broader cognitive functions. https://fbt.tums.ac.ir/index.php/fbt/article/view/1079 https://fbt.tums.ac.ir/index.php/fbt/article/download/1079/569

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 22 828 828 Roghayeh Panahi Department of Oral and Maxillofacial Radiology, School of Dentistry, Yasuj University of Medical Sciences, Yasuj, Iran Golrokh Niknam Department of Oral and Maxillofacial Radiology, School of Dentistry, Yasuj University of Medical Sciences, Yasuj, Iran Hassan Vafapour Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran 2023 09 16 2023 10 06 Purpose: The present study aimed to assess the Entrance Skin Doses (ESD) at the thyroid gland region and evaluate the local Diagnostic Reference Level (DRL) in panoramic radiography in a city (Yasuj, Iran). Materials and Methods: In the current study, 31 patients (17 women and 14 men) with a mean age of 33.90±16.49 years were included. To assess the ESD values, 3 thermoluminescence dosimeters (TLD-100) were attached to the thyroid gland region for each patient. The DRLs were estimated as the third quartile of the ESD values. The ESD variations among the different genders (men and women), devices, and age groups (children [5-10 years], adolescents [11-19 years], and adults [˃19 years]) were calculated. T-test, one-way ANOVA, and Bonferroni's post hoc test were used for parametric tests; Kruskal-Wallis and Spearman's correlation coefficient were used for the non-parametric tests. Results: Mean ESD and DRL values were obtained at 72±21 µGy and 0.091±0.02 mGy, respectively. For ESDs, there was no significant difference between different genders (men: 76±20 µGy and women: 69±23 µGy) as well as among the three investigated devices (P-value˃ 0.05). The ESD values of children were significantly lower than adolescent and adult patients (P-value<0.001); however, there was no statistically significant difference between the adolescent and adult patients (P-value =0.057). Conclusion: Compared to national/international, the DRL value in our study was relatively low; patient doses can be decreased in the panoramic examinations by increasing the knowledge of health workers of the radiation parameters, specifically operators. https://fbt.tums.ac.ir/index.php/fbt/article/view/828 https://fbt.tums.ac.ir/index.php/fbt/article/download/828/570

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 24 1438 1438 Zhamak Akhlaghi Tehran University of Medical Sciences Narges Hoseini Tabatabaei Tehran University of Medical Sciences Alireza Fallahi Hamedan University of Technology Fatemeh Eivazi Institute for Cognitive and Brain Sciences Neda Mohammadi Mobarakeh Tehran University of Medical Sciences Babak Babakhani Tehran University of Medical Sciences Saeed Masoudnia Institute for Research in Fundamental Sciences (IPM) Seyed Sohrab Hashemi-Fesharaki Pars Advanced Medical Research Center Jafar Mehvari Habibabadi Isfahan University of Medical Sciences Mohammad-Reza Nazem-Zadeh Tehran University of Medical Sciences 2025 08 13 2026 04 28 Purpose: Language and memory impairments are common in patients with temporal lobe epilepsy (TLE). Precise identification of language and memory areas is crucial for both identifying the seizure zone and predicting the preservation or recovery of cognitive functions after brain surgery. Functional MRI (fMRI), with its unique ability to elucidate the brain's compensatory mechanisms in response to epilepsy, aids in visualizing brain activity and reveals how different parts of the brain have adapted. Methods: We conducted a study on 22 left TLE and 13 right TLE patients, as well as 17 healthy control subjects, using task-based fMRI for memory and language mapping. This was done to examine the effective areas responsible for language and memory functions and detect abnormal activations. All participants underwent the CANTAB® neuropsychological test. Results: We discovered a significant increase in the participation of the left temporal lobe and parahippocampal gyrus in the encoding of non-verbal memory, and increased activation in the left precuneus and right parahippocampal gyrus during the retrieval of non-verbal memory (P < 0.05). In verbal memory tasks, increased participation of the frontal lobes in the encoding and retrieval of verbal memory, along with a significant contribution from the left cingulate gyrus during memory retrieval, indicates compensatory mechanisms (P < 0.05). Conclusions: Our findings suggest that, compared to healthy controls, TLE patients exhibited activation in more diverse brain regions during language tasks, yet achieved similar functional test results.   https://fbt.tums.ac.ir/index.php/fbt/article/view/1438 https://fbt.tums.ac.ir/index.php/fbt/article/download/1438/571

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 06 29 1525 1525 Akpama Egong University of Calabar, Calabar Blessing Ibe University of Calabar, Calabar Akwa Egom University of Calabar, Calabar Emmanuel Owolo 2. Tip Top Dignostic Center, Aduwawa, Benin Nneoyi Egbe University of Calabar, Calabar 2025 11 20 2026 06 16 Purpose: Defective tube or miscalibration has been implicated as the causes of Computed Tomography (CT) ring artifacts, implying that images acquired with such scanner should demonstrate ‘’ring(s)’’ artefact. However, ‘’ring(s)’’ artefact is frequently seen in moderate to severe hydrocephalic images but lacking on non-hydrocephalic images acquired using same machine. Hence we investigate causes of persisting ‘’ring(s)’’ artifact demonstrable on hydrocephalic CT images. Materials and Methods: The research included hydrocephalic and hydrocdphalous mimicking phantom CT images with ‘’ring’’ band(s) designated as inner, middle and outer band, and non-hydphalous CT image without ‘’ring’’ band divided into four quadrants - anterior right (AR), anterior left (AL), posterior right (PR), and posterior left (PL). The CT number was measured at four perpendicular points per ‘’ring’’ and randomly at four points in each quadrant. CT number measurement was carried out using DICOM image viewer version 22504.418.1.0., Region of Interest (ROI) = 0.10cm2), and with the outermost ‘’ring’’ band taken control, the relationships between CT number mean values between ‘’ring’’ band, and each quadrant was analyzed. Results: Dunnette multiple comparison test shows a significant difference in CT numbers between the Inner, Middle and Outer ‘’ring’’ bands with mean pixel values -1.194, -3.167, and 0.007 for hydrocephalous mimicking phantom image. Substantial difference in CT numbers between the Outer and Middle ‘’ring’’ band and similarity between the Outer and Inner ‘’ring’’ band on hydrocephalic image was also found with mean pixel values of 2.444, -1.593 and 2.222. For the non-hydrocephalic images, similarity in CT numbers across the four quadrants was noted with mean pixel values of AR = 17.89, AL=18.56, PR=17.17, and PL=14.00. Conclusion:  CT ring artefacts may be patient based, indicating onset of pathologic process and diagnostic indicator. https://fbt.tums.ac.ir/index.php/fbt/article/view/1525 https://fbt.tums.ac.ir/index.php/fbt/article/download/1525/572