As we launch the first issue of Frontiers in Biomedical Technologies (FBT) in 2026, it is my great pleasure to extend my deepest appreciation to all those who have contributed to the continued growth and advancement of the journal. I would especially like to acknowledge our domestic and international authors, whose trust in FBT and whose high-quality manuscripts form the foundation of our scientific achievements. Their contributions represent a diverse global community of researchers, and their commitment to excellence has significantly strengthened the journal’s impact and visibility. My sincere gratitude extends as well to our dedicated reviewers, whose constructive and rigorous evaluations ensure that every published article meets the highest standards of scientific quality. I also thank our readers for their continued engagement, and the editorial and executive teams for their sustained efforts, professionalism, and teamwork throughout the years.

The journal’s performance metrics clearly reflect a strong upward trend in both activity and visibility. According to our quantitative report, the number of submitted articles rose from 32 submissions in 2019 (when we seriously started) to 219 submissions in 2024, marking a remarkable expansion in the journal’s reach and recognition. As submissions increased, our acceptance rate naturally evolved—from 89% in 2019 to 45% in 2023—demonstrating both higher selectivity and our ongoing efforts to strengthen the scientific rigor of the journal.

FBT has also continued to expand its international academic footprint since being indexed in Scopus beginning in 2020. Recent citation analyses (2020–2024) show encouraging improvements in the journal’s citation performance, international collaboration, and the diversity of institutions and countries contributing to and citing our publications. These developments affirm FBT’s growing role as a trusted venue for impactful research in biomedical technologies.

As we move into 2026, we warmly invite researchers, scientists, clinicians, and industry experts to continue submitting their high-quality manuscripts to FBT. Your contributions are essential for advancing innovation in biomedical engineering and for supporting the journal’s mission to disseminate meaningful and influential research. In return, our editorial and executive teams remain dedicated to further enhancing the publication quality, review processes, visibility, and overall scientific impact of FBT. We look forward to a productive year filled with new ideas, strong collaborations, and continued academic progress.

Purpose: Respiratory infectious diseases often manifest as ground-glass opacity (GGO) or consolidation signs in the lungs. Artificial intelligence (AI) assisted systems utilizing data mining algorithms such as Waikato Environment for Knowledge Analysis (Weka) can be used for the detection and segmentation of these signs. In this study, we propose using Weka as a comprehensive data mining and machine learning tool to develop the most accurate models for detecting lung signs in chest CT images of patients with respiratory infectious diseases.

Materials and Methods: First, we mannually selected specific signs from chest computed tomography (CT) images from 600 cases using the Graphical User Interface (GUI) Weka plugin. We then trained the random forest algorithm based on different features and presented the best-combined model obtained for the automatic detection of the mentioned signs. Lastly, the model performance was evaluated with different metrics.

Results: Our findings indicate that the hybrid texture description features, including “Structure”, “Entropy”, “Maximum”, “Anisotropic” and “Laplacian” available in Weka, demonstrated the lowest Out-of-Bag (OOB) error rate, highest area under the ROC curve (AUC) value of 0.992 and accuracy of 98.1%.

Conclusion: By leveraging the combination of Weka features, we have successfully developed models for the detection and segmentation of lung signs associated with infectious diseases, from chest CT images. These findings contribute to the field of medical image analysis and hold promise for improving the diagnosis and treatment outcomes of patients with respiratory infectious disorders.

Purpose: There is a growing interest in the clinical application of new PET radiopharmaceuticals. This study focuses on using ⁶⁴Cu-DOTA-Trastuzumab for Positron Emission Tomography–Computed Tomography (PET/CT) imaging in gastric cancer patients. It aims to enhance the understanding of its bio-kinetic distribution and absorbed dose for safe and practical application in nuclear medicine.

Materials and Methods: The study was conducted at the Agricultural, Medical, and Industrial Research School (AMIRS), where ⁶⁴Cu was produced and purified. The radiopharmaceutical ⁶⁴Cu-DOTA-Trastuzumab was prepared, and three patients with confirmed Human Epidermal growth factor Receptor 2 (HER2)-positive gastric cancer underwent PET/CT scans at 1, 12 and 48 hours post-injection. Images were gained using a Discovery IQ PET/CT system and analyzed for an SUV. Bio-distribution was modeled using a two-exponential function, and absorbed doses were calculated using IDAC-Dose 2.1 software. CT doses were also evaluated.

Results: The study found that post-injection imaging at 12 hours or more provided superior image quality. The liver exhibited the highest cumulative activity, followed by the spleen and other organs. The effective dose estimates for ⁶⁴Cu-DOTA-Trastuzumab were within acceptable limits. CT dose calculations revealed that sensitive organs received higher doses.

Conclusion: This study successfully assessed the bio-kinetic distribution and absorbed dose of ⁶⁴Cu-DOTA-Trastuzumab in gastric cancer patients, demonstrating its safety and potential for clinical use. The optimal timing for PET/CT imaging and dosimetry data can inform clinical decision-making. Further research is warranted to explore the therapeutic potential of ⁶⁴Cu-DOTA-Trastuzumab and to establish clinical guidelines for its use.

Background: There are different types of hair loss known as alopecia. Various methods for treating Androgenetic alopecia (AGA) are being investigated in the preclinical stage using C57BL/6 mice affected by this condition.

Objective: The purpose of the study was to evaluate the effects of dihydrotestosterone (DHT) on the skin layers of male C57BL/6 mice, simulating a model of AGA using high-resolution ultrasound imaging.

Methods: Seven-week-old male C57BL/6 mice were selected for the study. To induce AGA, three of the mice received intraperitoneal injections of DHT at a dosage of 1 mg per day for five consecutive days, a known method for provoking hair loss via androgenic pathways. High-resolution ultrasound imaging at 40 and 75 MHz frequencies allowed detailed observation of skin layer changes due to DHT administration. Shear modulus and Young modulus were extracted using a dynamic loading throughout ultrasonography with 40 MHz frequency. Both control and AGA-affected groups were evaluated through structural imaging and were compared with histopathological results. Tissues were stained with Hematoxylin-Eosin (H&E) and so Trichrome Mason

Results: Ultrasound imaging revealed that the epidermis thickness was 0.22 mm in the control group compared to 0.31 mm in the AGA group at 40 MHz. At 75 MHz, these measurements were 0.10 mm for the control group and 0.20 mm for the AGA group. The dermis thickness measurements showed 0.30 mm in the control group and 0.70 mm in the AGA group at 40 MHz, while at 75 MHz, the thicknesses were 0.40 mm for the control group and 0.70 mm for the AGA group. H&E staining results aligned with these ultrasound findings, confirming increased epidermal and dermal thicknesses in the AGA group. Elasticity metrics indicated a shear modulus of 1.19 kPa for the AGA group and 6.70 kPa for the control group, while Young’s modulus demonstrated values of 6.47 kPa for the control group and 22.69 kPa for the AGA group. Further corroboration of altered tissue elasticity was provided by Trichrome staining, indicating significant changes in skin structure.

Conclusion: The administration of DHT in the C57BL/6 mice model leads to notable structural changes in skin layers, evidenced by an increased thickness of both the epidermis and dermis, along with diminished mechanical properties of skin elasticity.

Discovering the functional connections between human body parts can be beneficial for better control of brain-computer interface (BCI) systems. The brain, as the decision-making organ, controls all body parts to perform activities. In this study, the main objective is to investigate the relation of hand muscles and the effect of each muscle on another using electroencephalogram (EEG) signals. To this end, brain connections are extracted as influential components, and a convolutional network is used to calculate the effect of EEG signals on the connections between hand muscles. The relationships between EEG signal channels are computed using correlation methods, coherence, directed transfer function, Granger causality, and phase delay index. The relationships between electromyogram (EMG) signal channels are also calculated using Granger causality. Signals are recorded in two phases: rest and activity, and ultimately, the EMG signal activity is estimated solely using EEG signals.Simulation results estimate the correlation between the estimated and actual patterns for test data to be around 0.949, indicating a high correlation between the estimated outputs and actual values. According to the researches reviewed, there has been a lack of investigation into the EEG signal graph with muscular Discussions and its correlation with the EMG signal. Given that muscle actions necessitate input from multiple brain regions, it is anticipated that several areas of the brain will be engaged during this process. Therefore, employing graph theory may yield more profound insights into this interaction than traditional approaches, such as analyzing brain connectivity.

Keywords

Vital signal connections, brain-computer interface, regression, convolutional networks

Purpose: High-energy protons are generally used for neutron production by Pb, W, Li, Be, and Ta targets that are used for the Born Neutron Capture Therapy (BNCT) technique. Neutron production targets are destroyed by proton spallation (evaporation of nuclei). The purpose of this study is the investigation of neutron activation and proton spallation damage of converter targets using the MCNPX code, which is based on the Monte Carlo method.

Materials and Methods: The MCNPX code was used to extract the activation and spallation information of secondary particle production in Pb, W, Li, Be, and Ta targets. The neutron activation and proton spallation damage, including radioactive elements production in converter targets, was extracted from data in the MCNPX output file.

Results: Results showed that the highest probability of radioactive elements production by proton with low-level energy in the Ta target are 180Hf, 179Hf, and 178Hf, and in the Li target is 7Be, respectively. In addition, the most probable radioactive elements produced by 200, 800, and 1200 MeV proton spallation in lead target are 118Tl and 78Pt, and in tungsten target are 98Hf, 110Ta, and 111Ta, respectively. The calculations showed that the production of radioisotopes in reactions with neutrons is lower than the production in reactions with a proton beam, and with increases in the energy of the proton beam, production of the radioactive elements was increased.

Conclusion: The results illustrated that the radioactive elements are produced in W, Pb, Li, Be, and Te targets in the BNCT method, which should be avoided as radiation hazards.

Purpose: The complexity of Intensity Modulated Radiation Therapy (IMRT) technique increases dose uncertainties. Therefore, limiting the complexity can be effective in reducing uncertainty. This study aims to investigate the relationship between the modulation complexity score (MCS) and the number of monitor units (MUs), number of segments and gantry angles. Materials and methods: 60 patients with head and neck tumors were selected. Treatment planning was performed using the step-and-shoot IMRT technique on the RayStation treatment planning system (TPS). Treatment plans were divided into two groups including 30 simple (group 1) and 30 complex (group 2) treatment plans. The MCS formula was coded and implemented in the RayStation TPS to calculate the MCS. The MCS of complex and simple plans were compared. Then the relationship between the MCS and the number of monitor units (MUs), the number of segments, and the MCS per beam for different gantry angles in the two groups and all plans was investigated. Results: The Pearson correlation results for both groups and all plans showed a strong relationship between the number of MUs and the MCS (p<0.001). The R² was equal to 0.67 for all plans, 0.77, and 0.71 for the first and second groups, respectively. This indication of the strong correlation between MCS and MU in head and neck treatment plans for the first group plans shows a better correlation with the MU. The Pearson correlation results for both groups showed a strong relationship between the number of segments and the MCS (p<0.001). The R² value was 0.76 for the first group and 0.75 for the second group. The lowest MCS value or the highest complexity was related to the angles of 161-180 degrees, and the highest MCS value or the lowest level of complexity was for the gantry angles of 281-300 degrees. Conclusions: The results show correlation between the number of MU, the number of segments and the MCS in head and neck plans, so these items can be used to control complexity and reduce dose uncertainties.

Purpose: The pineal gland (PG) is a structure located in the midline of the brain, and is considered the main part of the epithalamus. There are reports on the role of this area for brain function by hormone secretion, as well as few reports on its role in brain cognition. However, little knowledge is available on the structural and functional connectivity of the PG with other brain regions, as well as its age and gender associations.

Materials and Methods: In this work, we used the diffusion and resting-functional MRI data of 282 individuals, in the age range of 19 to 76 years old. All participants were checked for their medical and mental health by a general practitioner, and the MRI data were collected using a 3 Tesla scanner. The diffusion data were analyzed using the Explore DTI software (version 8.3), and the fMRI data were analyzed using the CONN toolbox (version 18.0).

Results: Two white matter tracts connecting the PG Body to PG Roots and PG to Pons were extracted in this study. The mean FA of the two tracts were 0.26 ± 0.06 and 0.24 ± 0.08, respectively. Neither the FA values of the tracts nor their lengths, showed any associations with age and gender; However, with increasing age, the likelihood of successfully identifying the PG-Pons tract decreased. In functional connectivity analysis, five brain regions showed positive connectivity with the PG, including the superior temporal gyrus, middle temporal gyrus, brain stem, vermis, and the subcallosal cortex, and 25 regions showed negative connectivity. These connectivities did not show an association with gender, but some associations with age were observed.

Conclusion: This study is novel in estimating the functional and structural connectivity of the PG with other brain areas, and also in assessing the association of these connections with age and gender, which could help to increase our knowledge on the functional neuroanatomy of the pineal gland.

Objectives: Repairing aged restorations is a common clinical process in Dental Operations. The aim of this study is to evaluate the effectiveness of different bonding systems in terms of different adhesive and surface treatment systems for repairing aged resin composites.

Materials and Methods: Ninety resin composite discs were prepared and randomly assigned into three groups of 30, No surface preparation, Diamond milling roughness and Sandblasted. After 5000 heat cycles, each group was randomly divided into three subgroups of  Single Bond (3M), Composite primer (GC), and Schotch Bond Universal (3M) (n = 10). 180 composite cylinders of the new composite were prepared by squeezing the composite into a silicon tube. The samples were then subjected to 5000 heat cycles. Two-way ANOVA and Tukey tests were used for data analysis.

Results: In the unprepared group, the universal bond and composite primer micro-shear bond strength were significantly higher than the single bond group (p < 0.05). In the milling group, the universal bond micro-shear bond strength was significantly higher than the composite primer and single bond group (p < 0.05). In single bond adhesive, micro-shear bond strength of milling was significantly greater than the sandblasted and unprepared groups (p < 0.05). In the universal adhesive group, micro-shear bond strength of milling group was significantly higher than the sandblasted and unprepared groups (p < 0.05).

Conclusion: Micro-shear bond is affected by the surface preparation method and type of adhesive. Milling roughening with universal bond application showed the highest micro-shear bond strength.

Purpose: Real time motion tracking of thorax area of patient body is a main issue at various part of medical fields such as radiotherapy. Several strategies were proposed by using different monitoring hardwares. In this work a contactless method using optical camera is proposed to trace breathing motion by implementing virtual markers defined on chest area. A comprehensive algorithm has been developed to analyze the video frames and track each virtual point as real time.  Methods: In this wotk, Python program and its OpenCV library has been used for breathing motion, two dimensionally. Utilized database in this work are motion data taken from breathing motion of a real volunteer. The motion data was captured using cellphone optical camera and the gathered data was moved to in-room computer system by means of WiFi. It’s worth mentioning that 15 virutal test points were determiened using Artifical Intelligence concept of Python inside chest area. Results: Final results represent that the performance accuracy of monitoring proposed idea is acceptable. The chest area is determined automatically and will be variable for each patient, uniquely. Various normal and deep breathings was tested as real time at different respiration frequencies. As example, two dimentional motion displacements of a test point, are 4.75 and 7.15 mm for normal and deep breathing, respectively. Conclusion: The  main robusts of the proposed motion tracking method are simplicity, contactless and using virtual markers determination, while real infra-red markers are currently used clinically by locating on patient chest skin. 

Purpose: Functional Near-Infrared Spectroscopy (fNIRS) is a relatively novel tool that measures local hemodynamic changes, including oxygenated hemoglobin [Oxy-Hb], deoxygenated hemoglobin [Deoxy-Hb], and total hemoglobin [Tot-Hb]. Its safety, portability, non-invasiveness, and cost-effectiveness make it a preferred technique for designing Brain-Computer Interfaces (BCIs). This study aims to develop an accurate fNIRS-based BCI module for classifying mental tasks and the resting state.

Materials and Methods: Rather than relying on conventional statistical features, our approach utilizes nonlinear indices derived from a 2D Poincaré plot. These measures are computationally efficient and capable of revealing the underlying dynamics of the system. Our primary innovation lies in the development of a novel feature and selection method. We assessed mental task recognition in both subject-dependent and subject-independent classification modes.

Results: Our findings demonstrated a maximum accuracy of 93.75% for subject-specific style and 91.67% for subject-independent style.

Conclusion: In summary, the simplicity and high performance of the proposed framework suggest promising future directions for designing online fNIRS-based BCI systems.

Purpose: Reinforcement Learning (RL) is attracting great interest because it enables systems to learn by interacting with the environment. This study aims to enhance the RL algorithm to become more similar to human motor control by combining it with the Non-negative matrix factorization (NMF) method.

Materials and Methods: In the study, the signals recorded from six muscles involved in arm-reaching movement without carryinga certain weight.were pre-processed, and the optimal number of synergy patterns was extracted using NMF and the Variance Account For (VAF) methods. This, in turn, contributes to reducing the calculations. Subsequently, the robustness of the two-link arm model with six muscles was evaluated under various noise levels applied to the action coefficient matrix. Finally, the average synergy pattern was done on the mentioned arm model, and the RL algorithm controlled it by producing the action coefficient matrix.

Results: The average VAF% was 97.25±2.0%, and the number of synergies was four. The tip-of-the-arm model was able to reach the target after an average of 100 episodes.

Conclusion: The results indicated that the similarity in the extracted synergy patterns helps to model a system that is more similar to motor control. Additionally, the results of the synergistic patterns revealed that the two-link arm model with six muscles was suitable for the model. While controlling the model with the RL algorithm, the desired end-point position and path were achieved.

Brain Stroke is defined as the sudden death of the brain cells due to lack of blood circulation and form a lesion/mass in the cerebral parenchyma and led to loss of speech, weakness, or paralysis of one side of the body. If the diseases are detected in early stage it will be cured. The existing method does not provide efficient accuracy. In this paper two type of brain stroke lesions are classified such as infarct (lack of blood supply) and Haemorrhagic stroke (breaking of blood vessel). In this manuscript, Automated Brian Stroke Lesion Detection and Classification using Non-Contrast Computed Tomography and Dual Stage Deep stacked auto-encoder (DS-DSAE) with an Evolved Gradient Descent optimisation (EGDO) method is proposed to detect the brain stroke in early stage with great accuracy. In this the input image are taken from the slice level of Non-Contrast CT images dataset. Then the images are pre-processed, images are enhanced by removing skull regions, then the rotations are performed by mid-line symmetry process. Then the ROI region is extracted using wavelet domain. Then the images are classified using DS-DSAE and the weight parameters of the DS-DSAE are tuned using EGDO algorithm. Then the abnormal portions of the brain stroke lesions are detected and classified as acute infarct, chronic infarct and ischemic stroke, haemorrhagic stroke, and normal. The objective function is to increase the accuracy by decreasing the computational complexity.  The simulation process is executed in the MATLAB platform.  The proposed CLACHE-IDFNN-MBO attains higher accuracy 99.56%, High Precision 88.74%, High F-Score 92.5%, High Sensitivity 94.23%, High  Specificity 91.45%, lower computational time 0.019(s) and the proposed method is compared with the existing methods such as Fractional Order BAT Algorithm Fuzzy C with Delaunay triangulation (DT), social group optimization (SGO) and Fuzzy-Tsallis entropy (FTE), moth-flame algorithm (MFOA) and Kapur’s thresholding  respectively.

Background: For whole-body (WB) kinetic modeling based on a typical positron emission tomography (PET) scanner, a multipass multibed scanning protocol is necessary because of the limited axial field of view. Such a protocol introduces loss of early dynamics of the time-activity curve (TAC) and sparsity in TAC measurements, inducing uncertainty in parameter estimation when using prevalent least squares estimation (LSE) (i.e., common standard) especially for kinetic microparameters.

Purpose: We developed and investigated a method to estimate microparameters enabling parametric imaging, by focusing on general image qualities, overall visibility, and tumor detectability, beyond the common standard framework for fitting of data and parameter estimation.

Methods: Our parameter estimation method, denoted parameter combination-driven estimation (PCDE), has two distinctive characteristics: 1) improved probability of having one-on-one mapping between early and late dynamics in TACs (the former missing from typical protocols) at the cost of the precision of the estimated parameter, and 2) utilization of multiple aspects of TAC in selection of best fits. To compare the general image quality of the two methods, we plotted tradeoff curves for the normalized bias (NBias) and the normalized standard deviation (NSD). We also evaluated the impact of different iteration numbers of the ordered-subset expectation maximization (OSEM) reconstruction algorithm on the tradeoff curves. In addition, for overall visibility, a measure of the ability to identify suspicious lesions in WB (i.e., global inspection), the overall signal-to-noise ratio (SNR) and spatial noise (NSD_spatial) were calculated and compared. Furthermore, the contrast-to-noise ratio (CNR) and relative error of the tumor-to-background ratio (RE_TBR) were calculated to compare tumor detectability within a specific organ (i.e., local inspection). Furthermore, we implemented and tested the proposed method on patient datasets to further verify clinical applicability.

Results: With five OSEM iterations, improved general image quality was verified in microparametric images (i.e., reduction in overall NRMSE: 57.5, 71.1, and 56.1 [%] in the K₁, k₂, and k₃ images, respectively). The overall visibility and tumor detectability were also improved in the microparametric images. (i.e., increase in overall SNR: 0.2, 4.1, and 2.4; decrease in overall NSD_spatial: 0.2, 5.4, and 4.1; decrease in RE_TBR for a lung tumor: 17.5, 82.2, and 68.4 [%]; decrease in RE_TBR for a liver tumor: 255.8, 1733.5, and 80.3 [%], in K₁, k₂, and k₃ images, respectively; increase in CNR for a lung tumor: 1.3 and 1.0; increase in CNR for a liver tumor: 1.2 and 9.8, in K₁ and k₃ images, respectively). In addition, with five OSEM iterations, the differences in macroparametric images of the two methods were insignificant (i.e., overall NRMSE difference was within 10 [%]; differences in overall SNR, overall NSD_spatial, and CNRs for both tumors were within 1.0; and the difference in RE_TBR was within 10 [%] except for an exceptional case). For patient study, improved overall visibility and tumor detectability were demonstrated in micoparametric images.

Conclusions: The proposed method provides improved microkinetic parametric images compared to common standard in terms of general image quality, overall visibility, and tumor detectability.

Purpose: The process of making a decision based on available sensory information is called “Perceptual Decision Making”. The manner in which this decision is made has a direct impact on a person's social and personal relationships. Despite numerous studies in the field of perceptual decision making, there is still no robust system that can recognize people's perceptual decisions objectively. To this aim, this study aims to examine the relationship between EEG signals and perceptual decision making in healthy individuals.

Materials and Methods: The research employs an online EEG dataset based on visual stimuli, including faces and cars, obtained from 16 participants. Since there is no binary decision-making mode in the brain and there is an uncertainty in which each option has a special weight in decision-making and finally the option that passes a threshold is selected, this research has tried to incorporate this uncertainty into the final model to improve perceptual decision recognition system performance. For this purpose, a fuzzy radial basis function (FRBF) network was utilized.

Results: After extracting 26 features from the preprocessed EEG signals, Friedman’s non-parametric statistical analysis was performed, revealing that differences in the coherence of stimulus representations have a greater impact on an individual's decision-making process than spatial prioritization. Then, FRBF network classifier, with the extracted features from TP9 and TP10 channels as input, achieved an accuracy of 90.3% in classifying the test data as either a "face" or "car".

Conclusion: The classification accuracy results showed that the proposed method is an effective procedure for recognition of human decisions.

Purpose: People with Down Syndrome must be served special because they have an intellectual disability with abnormality in memory and learning, so, creating a model for DS recognition may provide safe services to them, using the transfer learning technique can improve high metrics with a small dataset, depending on previous knowledge, there is no available Down syndrome dataset, one can use to train.

Materials and Methods: A new dataset is created by gathering images, two classes (Down=209 images, non-Down=214 images), and then expanding this dataset using Augmentation to be the final dataset 892 images (Down=415images, Non-Down=477 images. Finally, using a suitable training model, in this work, Xception and Resnet models are used, the pretrained models are trained on Imagenet dataset which consists of (1000) classes.

Results: By using Xception model and Resnet model, it concluded that when using Resnet model the accuracy = 95.93% and the loss function =0.16, while by using Xception model, the accuracy =96.57% and the loss function =0.12.

Conclusion: A transfer learning is used, to overcome the suitability of dataset size and minimize the cost of training, and time processing the accuracy and loss function is good when using Xception model, in addition, the Xception metrics are the best by comparing with the previous studies.

Purpose: Dental caries can emerge anywhere in the mouth particularly in the interior of the cheeks and the gums. Some of the indications are patches on the inner lining of the mouth, along with bleeding, toothache, numbness and an unusual red and white staining. Hence, it is important to predict the presence of cavity at an early stage. The currently available manual method is inefficient and hence we provide an advanced method by using the deep learning concepts.

Materials and Methods: In this work, different types of algorithms such as Res Net, Deeper Google Net and mini VGG Net are to be used to predict the class of cavity at an early stage.

Results: A comparison between the accuracy of three different algorithms is given in this paper. Thus, by using efficient deep learning algorithms, it will be able to predict the presence of cavity and the class of cavity at an early stage and take necessary steps to overcome it.

Conclusion: In this work, a comparison between three different algorithms is given and proved that the efficient algorithm is the inception algorithm among the other algorithms and achieve an accuracy of about 98%, which is suitable for use in hospitals.

Purpose: Ionizing radiation exposure doses during radiological procedures may increase the patient dose; therefore, dose assessment is an important subject. The current study aimed to estimate the Effective Dose (ED), Risk of Exposure-Induced Death (REID), as well as Annual Per Capita Dose (APCD) in routine radiography procedures in Yazd province (Iran).

Materials and Methods: The data related to the exposure parameters and entrance surface air kerma (ESAK) of 9 public high-patient-load radiography centers (11 radiology devices) were collected from 783 patients. Five routine planar radiological examinations were included: lumbar spine, pelvis, abdomen, chest, and skull. The ED and REID values for each device and examination were obtained using a personal computer-based Monte Carlo (PCXMC, v. 2.0) software. The APCD was estimated by dividing the Annual Collective Effective Dose (ACED) by the Yazd population.

Results: The estimated mean ESAK values ranged from 0.26±0.11 mGy (chest examination) to 8.45±5.3 mGy (lumbar examination). The lumbar spine examination had the highest ED value (1.02 ± 0.75 mSv). The highest REID value for abdominal, chest, lumbar, pelvic, and skull examinations is associated with stomach (6.58±7.72), lung (2.36±2.79), stomach (7.03±6.11), colon (3.31±5.49), and other cancers (0.58±0.56). The ACED value due to the radiology examinations was obtained at 45.782 man-Sv.

Conclusion: Our results demonstrated that the dose variations among the patients were remarkably high. Choosing appropriate imaging parameters, reducing the frequency of unnecessary radiology examinations, and performing quality control procedures of radiology machines could reduce the patients' doses.

Objective: In Morocco, significant disparities exist in observing national radiation protection standards, particularly in conventional radiology for pediatric patients. This cross-sectional study aims to establish Moroccan diagnostic reference levels (DRLs) for pediatric thorax radiography.

Methods: Thorax radiography examinations of  208 pediatric patients (newborns up to 18 years old) from four public hospitals in Morocco were evaluated. Patient demographics (age, gender, weight), and scan parameters were recorded to calculate radiation doses using CALDOSE_X 5.0 software, quantifying entrance surface air kerma (ESAK) in mGy. The study samples were divided into five age groups (age <1 month, 1 month ≤ age< 4 years, 4 years ≤ age< 10 years, 10 years ≤ age< 14 years, and 14 years ≤ age < 18 years). The third quartile (P75) of calculated ESAK in mGy and kinetic energy released per unit mass (KERMA)-area product (KAP), in mGy.cm² for each group were analyzed. Statistical analysis was performed using SPSS version 21.

Results: The P75 values for ESAK (mGy) and KAP (mGy.cm²) diagnostic reference levels across age groups were: 0.61, 0.69, 0.68, 0.82, and 1.29 for ESAK, and 350.25, 566.07, 499.14, 950.62, and 1816.06 for KAP. The calculated regional DRLs for pediatric thorax radiography exceeded the published values for thorax protocols in some European countries. The irradiated surfaces significantly impacted the received doses of patients up to 10 years old (p-values of 0.004, 0.000, and 0.001).

Conclusions: Adapting the irradiation surface to patient morphology is crucial, requiring precise control over exposure factors, radiation field size, and protocol selection.

The integration of artificial intelligence (AI) into nuclear medicine has transformed diagnostic and therapeutic processes, yet the opaque nature of many AI models hinders clinical adoption and trust. This narrative review aims to synthesize the current landscape of explainable AI (XAI) in nuclear medicine, emphasizing its role in enhancing transparency, bias mitigation, and regulatory compliance for robust clinical integration. Key chapters cover the fundamentals of XAI in nuclear medicine; XAI applications in PET and SPECT instrumentation and acquisition; image reconstruction; quantitative imaging and corrections; post-reconstruction processing and analysis; and radiotherapy. The review concludes with a discussion of challenges, limitations, and future directions, advocating for interdisciplinary advancements to bridge AI innovation with practical utility in patient care.

Background: Terahertz (THz) imaging has emerged as a promising technique for non-destructive evaluation and imaging applications, offering unique advantages over traditional imaging modalities. This paper presents an overview of the current state of Terahertz Computed Tomography (THz-CT) and highlights the challenges faced in its implementation.

Objective: THz-CT utilizes electromagnetic waves in the terahertz frequency range to reconstruct three-dimensional images of objects with high resolution and penetration capabilities. The ability to visualize internal structures without the use of ionizing radiation has significant implications for various fields, including medicine, security, and material science.

Materials and Methods: Despite its potential, THz-CT faces several challenges that need to be addressed for its widespread adoption. Firstly, the limited availability and complexity of THz sources and detectors hinder the practical implementation of THz-CT systems. Efforts are being made to develop compact, efficient, and cost-effective THz sources and detectors to overcome these limitations.

Secondly, THz waves are highly susceptible to scattering and absorption by various materials, including water and certain organic compounds. This poses challenges in achieving accurate and artifact-free reconstructions, especially in applications involving biological samples. Researchers are exploring advanced signal processing techniques and novel imaging algorithms to mitigate these effects and enhance image quality.

Results: Furthermore, the relatively long acquisition times required for THz-CT imaging limit its real-time applications. Efforts are underway to develop faster acquisition methods, such as multi-view imaging and compressed sensing, to reduce acquisition times while maintaining image quality.

Lastly, the lack of standardized protocols and benchmarks for THz-CT imaging hinders the comparison and reproducibility of results across different systems and studies. Establishing common evaluation metrics and guidelines will facilitate the development and validation of THz-CT techniques.

Also, in addition to its various applications, terahertz medical imaging and medical microbiological detection plays a significant role in the diagnosis of several types of cancers, including skin, oral, breast, and colon cancers. One of the key advantages of terahertz radiation is its exceptional sensitivity to water content, enabling the creation of high-contrast images that effectively differentiate between normal and cancerous tissues. This capability proves instrumental in accurately identifying and assessing the presence of cancer in affected areas.

Conclusion: In conclusion, Terahertz Computed Tomography holds great promise for various imaging applications, but several challenges need to be overcome for its widespread adoption. Addressing the limitations associated with THz sources, scattering and absorption effects, acquisition times, and standardization will pave the way for the realization of the full potential of THz-CT in the future.

This review explores the integration of multimodal large language models (MLLMs) with medical imaging and omics data, highlighting their transformative potential in healthcare, particularly for disease diagnosis and treatment. By combining imaging techniques like MRI, CT, and PET with omics data including genomics, transcriptomics, and proteomics. MLLMs facilitate a holistic approach to understanding disease mechanisms at molecular and structural levels. Advanced AI models, such as deep learning and machine learning algorithms, enhance diagnostic precision by identifying biomarkers, predicting survival outcomes, and enabling targeted cancer therapies. The paper examines key applications, such as multimodal AI in cancer prognosis, single-cell analysis, and radiomics in precision medicine, while discussing challenges like data complexity and feature selection. The comprehensive review underscores the impact of MLLMs on disease management, paving the way for significant improvements in clinical decision-making and patient outcomes.

Robotic surgery has transitioned from phenomenon to norm in the medical field, particularly in minimally invasive surgery. Robotic-assisted surgery offers greater precision, quicker recovery, and better patient outcomes, but issues like astronomical costs, technical issues, and ethical issues prevent its adoption. Robotic surgery's advantages—greater precision, less invasive procedures, and better clinical outcomes—are outlined here while addressing issues to its adoption. New technologies like AI integration, autonomous technology, and tele-surgery are revolutionary but will have to be accompanied by strong regulatory frameworks. Technologists', clinicians', and policy makers' collaboration is important to patient safety and equitable access as robot surgery advances.