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.

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.

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.

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.

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.

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.

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 2^nd & 3^rd 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.

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 

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.

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.

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.

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.

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.

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 (CTDI_vol) 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), CTDI_vol, 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 CTDI_vol and the discrepancies ranged between 26% to 35%.

Conclusion: the CTDI_Vol 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

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.

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 SUV_max 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 SUV_max 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 SUV_max 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.

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.

Purpose: In this study, we propose a novel generalizable hybrid underlying mechanism m for mapping Human Pose Estimation (HPE) data to muscle synergy patterns, which can be highly efficient in improving visual biofeedback.

Materials and Methods: In the first step, Electromyography (EMG) data from the upper limb muscles of twelve healthy participants are collected and pre-processed, and muscle synergy patterns are extracted from it. Concurrently, kinematic data are detected using the OpenPose model. Through synchronization and normalization, the Successive Variational Mode Decomposition (SVMD) algorithm decomposes synergy control patterns into smaller components. To establish mappings, a custom Bidirectional Gated Recurrent Unit (BiGRU) model is employed. Comparative analysis against popular models validates the efficacy of our approach, revealing the generated trajectory as potentially ideal for visual biofeedback. Remarkably, the combined SVMD-BiGRU model outperforms alternatives.

Results: the results show that the trajectory generated by the model is potentially suitable for visual biofeedback systems. Remarkably, the combined SVMD-BiGRU model outperforms alternatives. Furthermore, empirical assessments have demonstrated the adept ability of healthy participants to closely adhere to the trajectory generated by the model output during the test phase.

Conclusion: Ultimately, the incorporation of this innovative mechanism at the heart of visual biofeedback systems has been revealed to significantly elevate both the quantity and quality of movement.

Purpose: Coronary Artery Disease (CAD), characterized by coronary artery stenosis—the narrowing of arteries supplying blood to the heart—is a leading global cause of morbidity and mortality. Timely detection and management of stenosis are crucial to preventing severe outcomes such as myocardial infarction and heart failure. Despite advancements in medical imaging, current diagnostic methods rely heavily on the manual interpretation of coronary angiograms, which is time-consuming, subjective, and prone to variability. To address these limitations, this study proposes an automated object detection-based framework for identifying coronary artery stenosis in medical imaging.

Materials and Methods: The study employs two state-of-the-art deep learning models, RetinaNet and EfficientDet D3, to detect stenotic regions in X-ray angiography images. A dataset of 8,325 annotated images from 100 patients with single-vessel CAD, sourced from the Research Institute for Complex Issues of Cardiovascular Diseases in Kemerovo, Russia, was used for training and evaluation. To enhance model performance, a comprehensive preprocessing pipeline was applied, including image resizing, data augmentation, and intensity normalization. These steps ensured robustness and generalizability across diverse imaging conditions.

Results: Both models demonstrated high accuracy in stenosis detection. RetinaNet achieved a mean Average Precision (mAP) of 93.2%, while EfficientDet D3 outperformed with an mAP of 96.6%. These results highlight the models' ability to accurately identify stenosis, even in noisy and variable angiographic images. The superior performance of EfficientDet D3 underscores its potential for clinical integration, offering precise and reliable stenosis localization.

Conclusion: This study presents a robust and efficient deep learning framework for the automated detection of coronary artery stenosis. By reducing reliance on manual interpretation and enhancing diagnostic accuracy, the proposed approach supports timely and informed clinical decision-making. This innovation has the potential to streamline diagnostic workflows, improve patient outcomes, and advance the application of artificial intelligence in cardiovascular healthcare.

Purpose: There is a known decline in brain volume with age, impacting cognitive health and increasing the risk of diseases such as dementia and Alzheimer's. Physical activity has been shown to have positive effects on brain structure and cognitive function with aging. Still, the association between motor function and brain volume in young adults remains unclear.

Materials and Methods: This study utilized high-resolution T1-weighted MRI images and motor function test results from 1082 healthy young adults aged 22-37, sourced from the Human Connectome Project Young Adult (HCP-YA). Motor functions were assessed using four tests: Endurance, Gait Speed, Dexterity, and Strength. Correlation analysis and multiple linear regression models were used to evaluate the association between motor functions and brain volumes, adjusting for demographic variables and Body Mass Index (BMI).

Results: Significant positive correlations were found between Endurance and Strength tests with multiple brain volumes. In contrast, the Dexterity test showed negative correlations reflecting intricate patterns of neural connectivity and plasticity, which may not directly correlate to brain volumes. No significant correlations were observed for the Gait Speed test, indicating that it may not be a sensitive indicator of brain health in younger adults. Multiple linear regression analyses revealed that total brain (β = 0.045, SE = 0.020), total gray matter (GM) (β = 0.035, SE = 0.016), left white matter (WM) (β = 0.058, SE = 0.025), right WM (β = 0.056, SE = 0.025), total WM (β = 0.057, SE = 0.025), and left accumbens (β = -0.072, SE = 0.031) volumes were significantly associated with motor function scores (p < 0.05).

Conclusion: Physical fitness, as measured by motor function tests, is significantly associated with brain structural integrity in young adults. These findings highlight the potential importance of physical activity in maintaining brain health, which could inform strategies to promote active lifestyles and prevent neurodegenerative diseases.

Migraine is a complex neurological disorder characterized by dynamic alterations in brain activity during multiple phases: interictal (baseline), preictal, ictal, and postictal. This study aims to model and differentiate these migraine phases using electroencephalogram (EEG) and a Hidden Markov Model (HMM). EEG signals were collected from each subject over several months through frequent, short sessions—often multiple times per day. The recordings were temporally aligned with self-reported symptom diaries, allowing for precise labeling of migraine phases. A comprehensive set of features was extracted from the EEG signals, including spectral, temporal, and nonlinear measures—such as Dynamic Mode Decomposition (DMD) and Katz Fractal Dimension (KFD)—across various frequency bands. Despite the limited number of participants, the dense long-term recordings captured multiple migraine episodes, enabling reliable phase modeling. The HMM identified distinguishable neural patterns corresponding to migraine states, suggesting the feasibility of temporal EEG modeling for clinical applications in personalized migraine management.

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.

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.

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.