Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 13 2 2026 05 01 290 293 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 13 2 2026 05 01 294 305 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 13 2 2026 05 01 550 572 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 Purpose: This study aims 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 prospects of this technology. Materials and Methods: This systematic review was conducted in accordance with PRISMA guidelines and protocol registered in PROSPERO (CRD420251154905). A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science, identifying 373 records. Following PRISMA guidelines, 40 studies met the 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 demonstrated superior radiosensitization with sensitizer enhancement ratios (SERs) of 1.25–1.48 and up to 450% increase in reactive oxygen species (ROS) in vivo, achieving ~70% tumor volume reduction without systemic toxicity. Silver NPs 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, including cisplatin and curcumin with nanocarriers, yielded SERs up to 4.29. The 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). Conclusion: 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 13 2 2026 05 01 306 316 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 13 2 2026 05 01 573 591 Mahshid Kiani Department of Nuclear Pharmacy, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran Saeed Farzanefar tehran university of medical siences Seyyed Soheila Mirabedian Department of Nuclear Medicine, Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran Mohsen Bakhshi Kashi Department of Nuclear Medicine, Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran Elisabeth Eppard University Clinic for Radiology and Nuclear Medicine, Faculty of Medicine, Otto Von Guericke University (OvGU), 39120 Magdeburg, Germany Nasim Vahidfar Department of Nuclear Medicine, Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran 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 13 2 2026 05 01 317 326 Maryam Samani https://orcid.org/0000-0002-5702-7082 Ali Gharekhani Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran Parastoo Farnia Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences , Tehran, Iran Bahador Makki Abadi Department of Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran 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 13 2 2026 05 01 592 600 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 13 2 2026 05 01 327 345 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 13 2 2026 05 01 346 357 Hamidreza 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 13 2 2026 05 01 358 364 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 13 2 2026 05 01 365 375 Atheer abdulhussain ali . Department of oral diagnosis college of dentistry. University of Baghdad.Iraq.; atheer.abdulhussain1106a@codental.uobaghdad.edu.iq; https://orcid.org/0009-0006-2805-5208. ;B.D.S,MSc.(oral histology) Enas Fadhil Kadhim Department of oral diagnosis college of dentistry. University of Baghdad.Iraq 2023 12 07 2024 01 23 Purpose: apply of tissue-engineering principles to generate simple and effective dental pulp capping material represent a promising therapeutic option for treating immature and mature tooth with compromised pulp vitality. The aim of the study was to evaluate the pathophysiological response of the pulp to (nano-chitosan/CaCO3) scaffold as potential novel  capping material on traumatically exposed pulp of rabbit incisors. Material and methods:24 lower central incisors of New Zealand rabbits were used, subdivided into two group of 12 teeth according to sacrificing time (1 and 4 week), in each group, six teeth were used as control group, the pulp is traumatically exposed and partially amputated left free of capping material, and the other six teeth used as experimental group ,were the amputated pulp capped with (nano-chitosan/CaCO3) ,the  cavity of both groups sealed with resin modified glass ionomere cement .animal were sacrificed and teeth were collected for histological examination  Results: At 1 week, both groups showed non-significant difference in inflammatory extent, high significant difference in calcific bridge formation (P=0.002) .and dentin morphology (P=0.002) . at 4 week period  there is non-significant difference in inflammatory response ,high significant difference in dentine bridge formation (P=0.002) but non -significant difference in the dentin morphology (P=0.06) group, (nano-chitosan/CaCO3) showed faster dentin bridge formation in both period compared to control group Conclusions: (nano-chitosan/CaCO3) composite is promising novel pulpotomy material  https://fbt.tums.ac.ir/index.php/fbt/article/view/899 https://fbt.tums.ac.ir/index.php/fbt/article/download/899/574

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 13 2 2026 05 01 376 394 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 13 2 2026 05 01 395 406 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 13 2 2026 05 01 407 415 Ghazal Etemadi Department of Medical Physics and Biomedical Engineering, Shahid Beheshti University of Medical Sciences, Tehran, Iran Ahmad Mostaar Department of Medical Physics and Biomedical Engineering, Shahid Beheshti University of Medical Sciences, Tehran, Iran 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 13 2 2026 05 01 416 423 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 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 13 2 2026 05 01 424 438 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 13 2 2026 05 01 439 446 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 13 2 2026 05 01 447 457 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 13 2 2026 05 01 458 471 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 13 2 2026 07 13 472 491 Faezeh Daneshmand-Bahman Department of Biomedical Engineering, Semnan University, Semnan, Iran Ateke Goshvarpour Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran 2024 08 18 2025 05 26 Purpose: Today, the human lifestyle has led to an increase in anxiety. Its diagnosis is usually made with questionnaires and by specialist physicians. Recently, objective techniques such as brain-behavior analysis have captivated the attention of scientists for the early detection of this disorder. This study aimed to provide a method for diagnosing anxiety based on electroencephalogram (EEG) signals. Also, presents a new methodology by examining different approaches to brain channel selection and feature extraction based on chaotic maps. Materials and Methods: The DASPS database was used, containing a 14-channel EEG of 23 people (10 men and 13 women, average age: 30 years). The self-assessment manikin was applied to divide anxiety into 2 and 4 levels. Firstly, four methods were assessed to select the optimal channel; two methods were based on the minimum coefficient of variation, and two methods were based on the maximum relative power. Then, Chebyshev’s chaotic map was reconstructed, and two features, including 1) the maximum density and 2) its corresponding sample, were extracted. Finally, the k-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers were applied. Results: The results indicated a maximum accuracy of 100% for both two/four-level anxiety detection. In addition, the K-NN outperformed the SVM classifier. Conclusion: It highlighted the role of some brain channels, as well as the classifier structure, in distinguishing anxiety levels. The outstanding result of the proposed algorithm nominated it as a suitable approach for anxiety detection. https://fbt.tums.ac.ir/index.php/fbt/article/view/1089 https://fbt.tums.ac.ir/index.php/fbt/article/download/1089/575

Tehran University of Medicadality 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