Computers and Electronics in Medicine (CEM) is a peer-reviewed interdisciplinary journal dedicated to advancing the field of medical technology through the integration of computer science, electronics, and healthcare. The journal serves as a premier platform for researchers, engineers, clinicians, and practitioners to share and disseminate cutting-edge research findings, innovative technologies, and practical applications at the intersection of computing and medicine. The journal publishes regular papers dealing with the following areas, but not limited to: - Medical Imaging - Bioinformatics and Health Informatics - Wearable Devices and Remote Monitoring - Telemedicine and Telehealth - Medical Robotics and Automation - Artificial Intelligence in Medicine - Biomedical Signal Processing - Mathematical Modeling and Simulation - Health Data Security and Privacy - Human-Computer Interaction in Medicine - Biomedical Engineering and Devices - Complex Systems and Network Analysis Computers and Electronics in Medicine emphasizes the importance of interdisciplinary collaboration to address complex healthcare challenges. The journal encourages submissions that bridge the gap between theoretical research and practical applications, fostering innovations that can be translated into clinical practice. By providing a comprehensive and inclusive platform, the journal aims to drive advancements that enhance patient care, improve diagnostic and therapeutic techniques, and contribute to the overall betterment of global health outcomes. The journal also supports the exploration of emerging technologies and methodologies that have the potential to revolutionize healthcare, such as blockchain for secure health data management, augmented reality (AR) and virtual reality (VR) for medical training and simulation, and the use of 3D printing in personalized medicine and prosthetics. Computers and Electronics in Medicine is committed to publishing high-impact research that advances the integration of computer science, electronics, and healthcare, fostering a multidisciplinary approach to solving the most pressing issues in modern medicine.
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Comparative Evaluation of Twenty Vision Transformer Variants for Six-Class Gastrointestinal Endoscopic Image Classification
This study compares 20 vision transformer variants for six-class gastrointestinal endoscopic image classification using a labeled subset of HyperKvasir. The evaluated classes were cecum, polyps, pylorus, retroflex-rectum, retroflex-stomach, and z-line. Repeated row totals from six confusion matrices indicated an evaluation set of 768 images. The experiment was configured for five-fold cross-validation with a maximum of 50 epochs and a random seed of 42; however, the numerical fold-level file contained results only for Fold 1. Learning curves supplied qualitative information for selected runs in Folds 2–5 and complete graphical coverage for EViT-B0, EViT-B1, and EViT-B3. In the available numerical results, MaxViT-Tiny achieved the highest accuracy (99.09%), F1 score (99.10%), precision (99.12%), and recall (99.09%), whereas TinyViT-21M achieved the highest AUC (99.98%). MaxViT had the highest mean family accuracy, while TinyViT had the highest mean family AUC. Accuracy and F1 were strongly correlated, but the association between accuracy and AUC was weak. The learning curves showed rapid convergence with model-dependent validation fluctuations, and the confusion matrices located most errors in cecum–polyp and polyp–retroflex-rectum pairs. These findings support further evaluation of hybrid local–global transformer designs. Complete fold-level results, patient-level split information, calibration analysis, and external validation are required before drawing conclusions about cross-fold or clinical generalizability.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
A Bistable Reaction-Diffusion Analysis of Tumour to Determine Oncolytic Virotherapy Outcome
Oncolytic viruses selectively replicate inside cancer cells and spread through tumour tissue as an infection front, so that the therapeutic outcome is set not only by how strongly the virus replicates but by whether and where that front advances, stalls, or collapses. We cast spatial oncolytic virotherapy as a bistable reaction–diffusion problem in which uninfected and infected tumour populations are coupled through infection and lysis, and we map the outcomes across geometry and parameter space. Linear analysis of the local kinetics shows that the tumour-free state is stable while the fully established tumour is unstable to viral invasion, so an infection front can be launched from a localized inoculation. Simulations in one dimension, on a planar disc, and in a radially symmetric spheroid reveal three sharply distinct outcomes: complete eradication, in which the front traverses the whole tumour; partial remission by wave pinning, in which the front stalls at a finite position and leaves a viable residual rim; and treatment failure, in which the infection dies out. A two-parameter regime map identifies the infection rate as the decisive control, with a finite window separating pinning at lower values from failure at higher values, whereas the bistability threshold of the tumour kinetics has almost no influence. The infected-cell spread rate behaves similarly, with eradication confined to an intermediate band. These results frame eradication as a tuned, geometry-dependent property of a travelling front and provide a compact map from measurable kinetic parameters to therapeutic outcome.
Comparing Explainable Artificial Intelligence and Deep Learning Models for MRI-Based Brain Tumor Diagnosis
Brain tumors represent one of the most life-threatening diseases, and their early and accurate detection is critical for improving patient outcomes. Magnetic Resonance Imaging (MRI) is the most reliable imaging technique for identifying brain tumors, yet manual interpretation by radiologists is time-consuming and prone to errors. To address these challenges, this study investigates the application of deep learning architectures, including Convolutional Neural Network (CNN), VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification. A publicly available MRI dataset consisting of glioma, meningioma, pituitary, and no-tumor cases was used. The models were trained and evaluated using accuracy, precision, recall, F1-score, ROC curves, and confusion matrices, while interpretability was assessed using Local Interpretable Model-Agnostic Explanations (LIME). Experimental results demonstrate that ResNet50 achieved the highest performance with 96.9% accuracy, followed closely by MobileNet at 96.6%, whereas CNN performed less effectively at 87.9%. The findings confirm that advanced deep learning architectures not only achieve high classification accuracy but also improve interpretability, thereby offering reliable and clinically applicable solutions for automated brain tumor diagnosis.
An Explainable Early-Warning Surrogate for Spiral-Wave Breakup in an Excitable Reaction-Diffusion Heart Model
Cardiac fibrillation is widely regarded as a transition from organized electrical activity to sustained spatiotemporal chaos, yet early indicators of this transition remain difficult to extract from sparse clinical measurements. We present a simulation-based, interpretable early-warning framework for predicting rotor breakup. A two-variable excitable-medium model generates a single pinned rotor that destabilizes into defect-mediated turbulence under gradually increasing tissue excitability. Phase-singularity counting provides the ground-truth breakup onset. Using signals from a sparse electrode grid, physiologically meaningful features are extracted over sliding windows during the organized phase and used to train a gradient-boosted decision-tree classifier, evaluated with simulation-grouped data splits to prevent leakage. The model predicts impending breakup with an area under the receiver-operating-characteristic curve of 0.997 and a median warning lead time of approximately six time units.Explainable additive attributions identify the dominant precursors as the spatial dispersion of activation timing, beat-to-beat cycle-length variability, and the dispersion of signal complexity across electrodes, all of which are consistent with restitution- and dispersion-driven destabilization theory. The framework yields transparent, mechanism-aligned warnings from device-measurable observables and points toward interpretable early-warning modules for implantable and wearable cardiac monitors.
Platelet Count as a Predictive Biomarker for Schizophrenia
Schizophrenia is a severe psychiatric disorder whose diagnosis remains largely dependent on clinical assessment, underscoring the need for objective and interpretable decision-support tools. This study investigates the use of platelet-based biomarkers and demographic variables for predicting schizophrenia through machine learning and explainable artificial intelligence. A structured dataset containing 151 observations with gender, age, platelet count, and diagnostic class was analyzed using a stratified train-test split. Nine classification algorithms were evaluated, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, K-Nearest Neighbors, Support Vector Machine, XGBoost, and LightGBM. Model performance was assessed using accuracy, precision, recall, F1-score, and receiver operating characteristic metrics. XGBoost achieved the best overall performance, with an accuracy of 0.652, recall of 0.773, F1-score of 0.680, and ROC value of 0.643. To improve clinical interpretability, SHAP and LIME were applied to examine global and local feature contributions. Both methods consistently identified platelet count and age as the most influential predictors, while gender had a comparatively smaller contribution. These findings suggest that explainable machine learning may provide a transparent framework for supporting schizophrenia prediction and for identifying potentially relevant biological markers. Further validation using larger, clinically diverse datasets is required before practical clinical deployment.
A Deep Q-Network-Assisted Artificial Hummingbird Optimized PID Controller with Derivative Filter for Blood Glucose Regulation
Blood glucose regulation is one of the most critical challenges in artificial pancreas systems due to the nonlinear characteristics of glucose-insulin dynamics and the difficulty of maintaining appropriate controller performance under varying operating conditions. This study proposes a hybrid control strategy combining the Artificial Hummingbird Algorithm (AHA) and a Deep Q-Network (DQN)-assisted PID controller with derivative filter for nonlinear blood glucose regulation. Initially, the controller parameters were optimally tuned using the AHA according to the Integral Square Error (ISE) performance criterion. Subsequently, instead of redesigning the entire controller, the DQN was employed to adaptively determine a weighting factor for the derivative gain during online operation, while the remaining controller parameters remained fixed. The proposed controller was evaluated using the nonlinear APMonitor blood glucose regulation model under identical simulation conditions. The simulation results demonstrated that the proposed approach improved blood glucose tracking performance by reducing the tracking error and oscillatory behavior compared with the conventionally optimized PID controller. Furthermore, the DQN training process exhibited stable convergence within a limited number of training episodes, indicating the effectiveness of the proposed adaptive learning framework. By combining offline global optimization with online adaptive learning, the proposed method preserves the simplicity and robustness of the classical PID controller while enhancing its regulation capability for nonlinear blood glucose systems. The obtained results indicate that the proposed hybrid strategy provides an effective and computationally efficient solution for intelligent blood glucose regulation.
A Virtual Pet for Detecting and Mitigating Autism Spectrum Disorder Stress
Individuals with Autism Spectrum Disorder (ASD) frequently experience elevated stress and are at higher risk for mood disorders such as anxiety and depression. Sensory over-responsivity, social challenges, and difficulties with emotional recognition and regulation contribute to such heightened stress. This study presents a proof-of-concept system that detects and mitigates stress through interactions with a virtual pet. Designed for young adults with high-functioning autism, and potentially useful for people beyond that group, the system monitors simulated heart rate, skin resistance, body temperature, and environmental sound and light levels. Upon detection of stress or potential triggers, the system alerts the user and offers stress-reduction activities via a virtual pet companion, including guided breathing exercises and interactive engagement. Through combining real-time stress detection with interactive interventions on a single platform, the system aims to help autistic individuals recognize and manage stress more effectively.
Enhancing Hospital Inventory Forecasting Accuracy through Hybrid and Ensemble Learning Models
Demand forecasting for medical and consumable supplies in healthcare institutions is a challenging problem due to irregular usage patterns, seasonality, sudden demand spikes, and data sparsity, and inaccurate forecasts may lead to stock-outs, excessive inventory costs, and disruptions in patient care. This study proposes an anomaly-aware, hybrid and ensemble-based forecasting and decision support framework for short-term hospital inventory demand prediction using real-world operational data obtained from a hospital inventory management system. The proposed approach integrates density-based anomaly detection, material-level behavioral feature extraction, supervised time series transformation, and a multi-model ensemble architecture combining linear models, tree-based methods, and boosting-based learners, with model selection and weighting performed via time-series cross-validation. To ensure operational robustness, a multi-layer fallback strategy incorporating classical exponential smoothing and conservative heuristics is employed for data-scarce scenarios, and an interpretable rule-based forecast confidence score together with an integrated ABC–XYZ segmentation scheme is used to directly link forecasts with inventory control policies. Experimental results on real hospital inventory data demonstrate that the proposed framework significantly improves forecasting stability and accuracy compared to single-model approaches, particularly for heterogeneous and irregular consumption patterns, while providing a practical, explainable, and operationally actionable solution for hospital inventory management.
Benchmarking State-of-the-Art Vision Transformer Architectures for the Automated Classification of Pigmented Skin Lesions
Skin cancer represents an escalating global public health challenge where early detection is paramount, potentially increasing five-year survival rates to 99%. While dermoscopy improves diagnostic sensitivity, its effectiveness often depends on clinician experience and is subject to inter-observer variability. To address these limitations, this study presents a rigorous comparative analysis of four state-of-the-art Vision Transformer (ViT) architectures, DeiT III-Base, Swin-Base, ViT-Base, and PiT-B, for the automated classification of pigmented skin lesions. We utilized the HAM10000 dataset (n=10,011) and implemented a stratified 70-15-15 split to ensure balanced training, validation, and testing phases. Images were resized to 224×224 pixels and normalized using ImageNet parameters, while transfer learning was employed to stabilize training and enhance generalization. Experimental results indicate that DeiT III-Base achieved superior diagnostic efficacy, reaching an accuracy of 92.04% and an F1-score of 85.44%. Furthermore, computational evaluation revealed that DeiT III-Base and ViT-Base offered highly efficient clinical throughput with sub-millisecond inference times (0.5674 ms and 0.5459 ms, respectively), whereas PiT-B exhibited the lowest computational workload (21.1067 GFLOPs). These findings underscore the viability of attention-based paradigms as robust real-time Computer-Aided Diagnosis (CAD) tools. Future research will explore the integration of multi-modal patient data and Explainable AI (XAI) to foster transparency and clinical trust.
Design and Development of a Low-Cost EMG-Controlled Prosthetic Hand
This study focuses on the design, development, and enhancement of a cost-effective myoelectric prosthetic arm intended for daily functional use. The initial prototype was fabricated using Fused Deposition Modeling (FDM) 3D printing technology with acrylonitrile butadiene styrene (ABS) as the base material. This approach enabled the creation of a lightweight, portable prosthetic arm with a human-like appearance and six degrees of freedom, allowing for the execution of essential daily activities. The actuation mechanism is based on an artificial tendon-driven system inspired by prior works such as the Vanderbilt Hand and the prosthetic hand developed at Hitit University. The tendon-driven structure allows for coordinated finger movement while preserving mechanical simplicity. Actuation is achieved using standard servo motors, controlled by surface electromyography (sEMG) signals acquired from the user’s forearm muscles. The initial version of the device was constructed with a material cost of approximately 250, achieving a grip force of around 3N per finger and a complete actuation cycle time of approximately 0.4 s. Despite demonstrating satisfactory functional performance, early user evaluations revealed challenges related to control intuitiveness and system integration, as reflected in user feedback surveys. To address these limitations and enhance usability, several technological upgrades were implemented. The original microcontroller was replaced with an Arduino Mega, and an ESP8266-07 Wi-Fi module was integrated to enable wireless communication. These enhancements significantly improved data transmission, real-time signal processing, and remote monitoring capabilities.
Exploring the Chemical Space of BACE-1 Inhibitors: Structure-Based Prediction with Deep Learning and Machine Learning
The identification of effective inhibitors targeting β-site amyloid precursor protein cleaving enzyme-1 (BACE-1) is crucial for developing therapeutic strategies for Alzheimer’s disease. This study developed a structure-based computational framework for predicting BACE-1 inhibitory activity using both deep learning and conventional machine learning techniques. A publicly available BACE-1 dataset with chemical structures defined in SMILES (Simplified Molecular Input Line Entry System) format was subjected to feature extraction using the RDKit program. Global molecular characteristics and substructural information were captured using both molecular fingerprint representations and physicochemical descriptors. Circular (Morgan/ECFP4) fingerprints, RDKit fingerprints, and MACCS keys were used to encode molecular substructures into binary vectors. Subsequently, Support Vector Machines (SVM), k-Nearest Neighbors (kNN), deep neural networks (DNN), and enhanced deep neural networks were trained and validated using these features under the same experimental conditions. Confusion-matrix analysis and standard classification metrics (accuracy, precision, recall, and F1-score) were used to assess the model’s performance. Deep learning models outperformed traditional machine learning techniques in capturing intricate nonlinear structure–activity correlations, according to comparison research. The proposed enhanced DNN demonstrated balanced precision and recall across both classes and achieved an accuracy of 0.99 on a test set of 303 molecules, including 138 active inhibitors and 165 inactive non-inhibitors. All things considered, these results imply that deep learning models, in conjunction with molecular fingerprints, offer a robust and reliable approach to BACE-1 inhibitor prediction and could accelerate early-stage virtual screening. All experiments were conducted using a fixed random seed and a held-out random split to ensure reproducibility.
Comparison of Artificial Intelligence Applications of EEG Signals in Neuroscience
In recent years, there has been a growing interest in the artificial intelligence (AI)-based analysis of electroencephalography (EEG) signals. This surge has made the potential of EEG more evident, both in monitoring cognitive states and in the early diagnosis of neurological disorders. This review systematically evaluates the academic literature from the past decade focusing on the processing of EEG signals through machine learning (ML), deep learning (DL), and other alternative techniques. The study compares personalized ML models (e.g., SVM, Random Forest) with wavelet decomposition–based optimized approaches and further analyzes the performance of Hilbert transform–based Convolutional Neural Network (CNN) architectures, label-free autoencoder frameworks, and multi-architecture DL systems in contemporary brain–computer interface (BCI) applications. In addition, incremental learning models based on multimodal data fusion are reviewed in the context of diagnosing disorders such as Alzheimer’s disease and epilepsy. The findings indicate that EEG–AI integration holds substantial potential for both research and clinical applications.
Evaluating the Performance Disparity and the Role of Gender-Aware Approaches in Machine Learning Based Disease Detection
Machine Learning (ML) is gaining attraction in medical research due to its ability to identify unnoticeable patterns by the human eye. However, concerns about fairness in ML models, particularly performance differences across groups, are growing. This study, therefore, focuses on evaluating the performance disparity and the role of gender-aware approaches in ML-based disease detection. It uses the gender-aware approach and introduces its two new variants by testing them on nine different disease datasets. Intensive experimental evaluations reveal that the detection performance can increase up to an F1-score of 1.0, depending on the nature of the dataset at hand. On the other hand, the genderaware approach is successful in mitigating the performance disparity only in three out of nine cases. The variants relying on a crossing-over fashion can capture the relationships and different patterns in some cases, but often fall behind the gender-aware approach. This research distinguishes itself through the use of a significant number of datasets and implemented pipelines, of which two are employed for mitigating performance disparity in disease detection for the first time in the literature. The findings of this study, therefore, make important contributions to the field of disease detection in terms of the aforementioned aspects.
Engineering applications in medicine and biology
Classification of Breast Cancer with Breast X-Ray Images via Convolution Neural Networks, Vision Transformers and AlexNet
Breast cancer is one of the most dangerous types of cancer, affecting many people long-term and leading to death. For this reason, it has become a frequently studied and emphasized topic in the medical field. Furthermore, significant advances in computer science have attracted the attention of the medical world, and computer science has also been incorporated into this challenging disease process to address it. The rapid development of artificial intelligence in recent years has led to a rapid increase in research on breast cancer. Numerous AI-based models have been developed to prevent human errors and to assist and support researchers in decision-making. In this study, one of these models was developed, and three different deep learning (DL) models were proposed to classify breast cancer as breast cancer-negative and breast cancer-positive. The study was adapted for computer vision (CV) using a Kaggle dataset called Breast Cancer, consisting of 3,383 breast tumor mammography images; the labels are 0 and 1, respectively, and the image dimensions are 640 x 640 pixels. In this study, three models were trained to classify breast cancer images: a Convolutional Neural Network (CNN), VisionTransformers (ViT), and AlexNet, trained with 45, 75, and 50 epochs, respectively. HuggingFace Space was used with these three models to classify breast cancer. The HuggingFace web application provided breast cancer classification based on the three models. Performance metrics, accuracy, loss, and execution time outperformed the CNN model, achieving a more optimal execution time (807.82 seconds), accuracy (0.9544), and loss (0.1078). The model has achieved significant success in breast cancer, and with further refinements, it is anticipated that the model will be suitable for use as a decision support system.
Adaptive–Scaled Digital Watermarking in Color Medical Imaging
Nowadays digital environment, the rapid duplication and unauthorized use of color images shared online have led to increasing concerns regarding copyright infringement and data security. Therefore, the development of effective and robust methods for protecting digital content has become critically important. In this study, an integrated digital watermarking method is proposed to ensure copyright protection and data security for both color and medical images. In the proposed approach, watermark embedding is performed by jointly applying Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) on the blue (B) color channel of the cover image. The watermark image is preprocessed using a dimension reduction technique and optimized through the Grey Wolf Optimization (GWO) algorithm. The optimized watermark is then embedded into the cover image, and inverse transformations are applied to obtain the watermarked image. The performance of the proposed algorithm is evaluated on standard test images under various attack scenarios. Imperceptibility is assessed using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), while robustness is measured using the Normalized Correlation (NC) and Bit Error Rate (BER) metrics. Experimental results demonstrate that the proposed method achieves high imperceptibility and successfully preserves watermark information under different attacks. Comparisons with related studies in the literature indicate that the proposed approach is competitive and, in some cases, superior in terms of both imperceptibility and robustness. Consequently, the proposed method provides an effective and reliable digital watermarking solution for color images.
TinyML-Based Machine Learning System for Multi-Class Ear Condition Classification
This study presents a TinyML-based machine learning system for multi-class ear condition classification on an edge device, designed to process images captured from a digital otoscope camera. Utilizing a dataset comprising five categories, Normal, Acute Otitis Media (AOM), Cerumen Impaction (CI), Chronic Otitis Media (COM), and Myringosclerosis (MYS), the proposed system performs near real-time classification directly on a resource-constrained microcontroller. The model was developed and optimized using the Edge Impulse platform and deployed as a quantized TinyML library. The system architecture incorporates lightweight convolutional neural networks (CNNs) and the EON™ Compiler to ensure efficient memory usage while maintaining high diagnostic performance. Experimental results demonstrate a validation accuracy of 97.5% and a testing accuracy of 96.31%, with a peak RAM footprint of only 240.3K and an inferencing latency of 1482 ms. These findings highlight the potential of TinyML for portable, low-power medical applications, providing a foundation for privacy-preserving, GDPR-compliant, on-device diagnostics in auditory healthcare without reliance on cloud infrastructure.
Classification of Brain MRI Images using Deep Learning: The DeiT3 Model and the Use of Feature Fusion Methods
Brain tumors are among the diseases that can seriously threaten human life and can be fatal. Early diagnosis of brain tumors plays a crucial role in the treatment process of the disease. However, accurately and quickly diagnosing this disease remains one of the significant challenges of modern medical technologies. Currently, advanced imaging techniques such as magnetic resonance imaging (MRI) are generally used for detecting brain tumors. This study proposes an artificial intelligence-based diagnostic approach using MRI images that include brain tumor types and consist of four classes. The proposed approach includes preprocessing, model training, feature fusion, and selection as final steps. In the preprocessing step, Grad-CAM and LBP techniques are applied to the original dataset, resulting in a total of three datasets, including the original one. These datasets are then trained with the DeiT3 model to obtain three separate feature sets (original, Grad-CAM-based, LBP-based). The feature sets are fused using a feature fusion technique, and the performance of the combined sets is evaluated using SVM methods. Feature selection methods (Chi2, Relief) are applied to the best-performing Grad-CAM & LBP-based feature set to highlight the most efficient features. Experimental analysis results show that a success rate of 99.5% was achieved using the SVM method.
Towards Robust CAD Systems for Digital Pathology: Evaluating Transformer-Based Backbones for Breast Cancer Classification
Breast cancer is one of the greatest global health burdens today and demands accurate diagnosis because of the vast histological variety. CNN-based systems had been the dominant technology in Digital Pathology, but with their inability to create a global representation has allowed other technologies such as Vision Transformers to compete. This paper evaluate the performance of three different transformer-based backbone architectures (DeiT Base, Swin Base, and ViT Base) for classifying breast histopathological images into eight granular classes using the BreaKHis database. To facilitate this comparison, we utilize transfer learning and distinct data augmentation methods. Each architecture was fine-tuned to classify four benign and four malignant subtypes with a minimum reported accuracy of 94%, with Swin Base performing more optimally than either of the other two approaches, obtaining highest reported accuracy of 0.9511 and an F1 score of 0.9434. The unique design and shifted windowing processes of Swin Base have allowed this architecture to capture detailed nuclear information as well as the larger context regarding breast cancers, to an extent greater than the other two architectures. Additionally, we provide an in-depth study of confusion matrices in conjunction with high classification accuracy, even when dealing with minor morphological overlap, to further support their claim regarding the ability of Swin Base and the remaining transformer architectures to successfully differentiate between histologically similar classes.
Determinants of Viewer Engagement in Health and Sports Videos: A Quantile Regression Forest Machine Learning Approach Applied to Reformer Pilates Content
This study assessed the quality, reliability, and popularity of Reformer Pilates videos on YouTube, evaluating educational value, credibility, and viewer engagement using standardized scoring while exploring factors affecting popularity. On November 1, 2024, a YouTube search for “Reformer Pilates“ identified the top 70 most-viewed videos. Videos were excluded if irrelevant, promotional, or under 25 seconds. Video quality and reliability were assessed using the Global Quality Scale (GQS), Modified DISCERN (mDISCERN), and JAMA benchmarks. Viewer engagement and popularity were measured with the Video Power Index (VPI). Factors associated with engagement were analyzed using Quantile Regression Forest modeling. Most videos (87.1%) were uploaded by individual channels, 12.9% by Pilates studios. Average GQS (3.06), mDISCERN (3.09), and JAMA (2.61) scores indicated moderate quality and reliability. Videos claiming instructor expertise had lower mDISCERN scores (p = 0.005). Studio-recorded videos had higher GQS scores than home-recorded videos, though not statistically significant. Quantile Regression Forest analysis indicated that mentioning safety information, shorter video duration, and higher GQS scores were among the factors associated with higher viewer engagement (VPI). Older videos tended to exhibit lower engagement levels, reflecting a preference for newer content. Findings highlight the importance of high-quality, concise, and safety-focused Reformer Pilates videos, as these characteristics are associated with higher engagement and popularity. Content creators may benefit from prioritizing these factors to attract and retain audiences, underscoring YouTube’s role in delivering potentially reliable Pilates content.
A Chaos-Based Encryption Scheme for Secure Medical X-ray Images
Data privacy in healthcare system is getting more importance day by day. In this study we introduce a novel chaos-based encryption scheme for medical X-ray images. We used a double Lorenz map as chaotic system to produce random key streams in confusion phase and in diffusion phase. We also used hash value of the original file to determine initial parameters of second chaotic map. So, our scheme is highly resistant to differential attacks. In confusion phase, we employed row-based and column-based shift operation to confuse original data. In diffusion phase we used XoR operation to encrypt data. Since Lorenz map has 3 dimensions, we used each one for different purposes like X dimension is for row-based shift operation, Y dimension is for column-based shift operation, Z dimension is for data distribution, XoR operation etc. On average, our scheme generates a NPCR value exceeding 99.66% and an UACI value of approximately 33.39% when there is a one-pixel alteration in the plaintext. Additionally, it yields an average information entropy value greater than 7.9976. Since our method utilizes a double Lorenz map, it is resilient against brute force attacks. The results from our tests and analyses indicate that our schema is pretty fast, dependable, resilient, practical, and effective. It serves as a solid encryption scheme option for medical images.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Deep Learning in Robot-Assisted Surgery: A Conceptual Framework for the da Vinci System
This study proposes a conceptual framework for integrating deep learning into the da Vinci Surgical System. The framework was developed after identifying common applications and challenges through a systematic review. It combines multiple data types, including visual, kinematic, and physiological signals, into a closed-loop system. This system includes four core components: data acquisition and fusion, deep learning-based analysis, adaptive control and feedback, and continuous skill assessment. These components interact to support real-time surgical guidance and personalized training, aiming to improve surgical outcomes. To inform the framework, a systematic search of the PubMed database was conducted, focusing on studies that combine deep learning with the da Vinci system. Two major application areas were identified: the first involves autonomous or semi-autonomous instrument control and image-guided navigation, and the second covers surgical skill assessment and workflow analysis. While existing studies demonstrate the potential of artificial intelligence in robotic surgery, they also reveal technical and practical limitations. By analyzing these common approaches and obstacles, this study provides a structured foundation for future research. The proposed framework offers a unified view that connects data processing, intelligent decision-making, and feedback, paving the way for smarter systems that assist both in real-time procedures and long-term training, and helping bridge the gap between advanced robotic hardware and cognitive support in surgical environments.
AI-Driven Classification of Anemia and Blood Disorders Using Machine Learning Models
Anemia and other blood disorders are serious global health issues affecting millions of individuals. These conditions, often triggered by insufficient hemoglobin or red blood cells, can manifest through symptoms like fatigue, weakness, and reduced immune function. When such disorders progress into advanced stages, they can compromise organ function and overall quality of life making early diagnosis especially critical. In recent years, as the value of prompt detection has become increasingly clear, artificial intelligence (AI) and autonomous diagnostic technologies have begun to take center stage in the medical community. Machine learning models excel at parsing complex datasets and generating accurate, rapid assessments, thus offering clinicians robust decision-support tools. Through these AI-driven methods, healthcare professionals can better interpret patients’ blood metrics and clinical indicators, enabling them to identify diseases at earlier stages and develop more effective treatment strategies. This study proposes a machine learning–based approach to classify various types of anemia and related blood disorders, including iron deficiency anemia, leukemia, and thrombocytopenia. We trained five contemporary algorithms Decision Tree (DT), Random Forest (RF), CatBoost, Gradient Boosting (GB), and XGBoost using critical blood parameters such as white and red blood cell counts, hemoglobin levels, and platelet counts. Notably, Gradient Boosting emerged as the most accurate model, achieving an impressive 99.19% accuracy rate. These findings underscore how AI-powered autonomous diagnostic systems have the potential to revolutionize hematology by facilitating earlier and more precise disease detection.
A Modified Transmission Line Modelling Approach for Bioheat Transfer
This study presents a modified Transmission Line Modelling (TLM) method for simulating bioheat transfer in biological tissues, with a particular focus on accounting for non-unidirectional blood flow. While the Pennes bioheat equation has been widely used for such problems, it assumes unidirectional perfusion and may overlook important thermal effects introduced by non-unidirectional blood flow. To address this limitation, a TLM-based formulation of the Klinger bioheat equation known for incorporating flow directionality is developed and implemented for one-dimensional (1D) problems. A novel TLM cell model is introduced to represent the governing equation and its associated boundary conditions. Validation is performed through a 1D multilayer human tissue model exposed to electromagnetic (EM) fields at multiple 4G carrier frequencies. Comparative results with the Finite Element Method (FEM) show strong agreement, especially under steady-state conditions. Furthermore, the proposed model reveals that the impact of non-unidirectional blood flow becomes more pronounced in thicker tissue layers, confirming the importance of incorporating convective terms. This work demonstrates that the modified TLM approach provides a stable, efficient, and more physiologically accurate method for bioheat simulations.
Deep Learning-Based Detection of Abdominal Diseases Using YOLOv9 Models and Advanced Preprocessing Techniques
Artificial intelligence has emerged as a transformative tool in medical imaging, enabling automated diagnosis and analysis across various domains. While significant advancements have been made in abdominal imaging, many studies struggle to achieve robust detection of diseases. The complexity and variability in abdominal structures present unique challenges for traditional machine learning models, necessitating the adoption of more advanced object detection frameworks. Motivated by these challenges, this study focuses on leveraging the YOLOv9 object detection architecture to enhance the identification of abdominal diseases using the TEKNOFEST 2022 Abdomen Dataset. Advanced preprocessing techniques, including CLAHE (Contrast Limited Adaptive Histogram Equalization) and Gaussian noise augmentation, were applied to improve image contrast and model robustness. The dataset was processed into YOLO-compatible formats, and multiple training configurations were evaluated using YOLOv9c and YOLOv9s variants. These configurations included variations in batch size, optimizer type (SGD and Adam), dropout rate, and frozen layers. Among the configurations tested, the YOLOv9s model with 32 batch size, SGD optimizer, and a 35% dropout rate demonstrated the best performance, achieving a Recall of 0.7698, Accuracy of 0.7698, and F1 Score of 0.8228. The highest mAP50 of 0.9385 was observed with the YOLOv9c model trained using the Adam optimizer and a 35% dropout rate. Confusion matrix analysis revealed strong detection capabilities for conditions like acute cholecystitis and abdominal aortic aneurysm. This study highlights the potential of YOLOv9 models in medical imaging and emphasizes the importance of high-resolution datasets and advanced feature extraction techniques for improving diagnostic accuracy in abdominal disease detection. These findings lay a foundation for the development of reliable and efficient AI-driven diagnostic tools.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
Investigation of the Effect of Alloying Elements on the Density of Titanium-Based Biomedical Materials Using Explainable Artificial Intelligence
Titanium alloys are widely preferred in the healthcare sector as biocompatible materials due to their superior properties such as low density and exceptional mechanical strength. Their low density provides lightweight solutions, and their density is closer to that of human bone compared to other metallic alloys with similar strength. This similarity facilitates a balanced load distribution between the bone and the implant, enhancing biomechanical compatibility. This study investigates the effects of alloying elements on the density of titanium-based biomedical materials using a computational materials science approach. A total of 72 different compositions of Ti-Al-V alloys were modeled using JMatPro software, and their densities were simulated at room temperature (25°C). The simulation produced a comprehensive dataset, which was utilized to train an explainable artificial intelligence (XAI) model. Advanced interpretability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plots (PDP), were employed to elucidate the influence of each alloying element on the density. The dataset was analyzed using an XAI-based regression model implemented with the Artificial Neural Network (ANN) algorithm. The interpretability graphs provided insights into the individual contributions of the alloying elements, revealing their positive or negative effects on the density. The findings offer a deeper understanding of the role of alloying elements in optimizing the performance of titanium-based biomedical materials, particularly in achieving lightweight designs. This study highlights the potential of integrating computational material modeling with explainable AI to advance the design and development of high-performance lightweight materials for biomedical applications.
Bibliometric Analysis of Publications on Clinical Studies Leveraging Natural Language Processing During 2000 - 2023
The number of clinical studies using natural language processing is quite large. Therefore, it is important to examine in depth the development of clinical studies using Natural Language Processing over the years. However, there are a limited number of studies in the literature examining the research status of this field. The article presents a bibliometric analysis of studies on the keywords "clinical AND studies AND natural AND language AND processing" indexed in Scopus between 2000 and 2023. This study aims to evaluate academic outputs in the relevant field quantitatively, make sense of the data, reveal the state of scientific knowledge in the field, and give scientists a general perspective on the subject. Bibliometrix and Microsoft Excel programs were used for bibliometric analysis. Nineteen thousand two hundred seventy-three different authors identified a total of 4535 studies. 77.5% of these studies were research articles (3516), 14.8% were conference papers (669), 6.8% were reviews (307), and 0.9% were book chapters (43). Journal of Biomedical Informatics was the journal in which the most studies were published, with 226 articles. Only the United States (2637) contributed 58.1% to the studies. Liu, H. was the most prolific author, with 85 articles. Harvard Medical School was the most productive institution, with 304 studies. The most cited article was Discontinuation of Statins in Routine Care Settings, A cohort study.
Collaborative Care: Multi-Agent Systems in Healthcare
Everyday thousands of IoT devices are added to networks globally. Medical IoT (IoMT) is a subdomain, which has had much success over recent years and is expanding rapidly. However, interoperability is poor with IoMT devices, and even the sharing of basic patient data is generally poor between healthcare systems. There is considerable waste and unnecessary duplication of data across devices, which grows exponentially across healthcare systems. Multi-agent systems have the potential to reduce waste and expenditure while improving efficiency and personalizing patient care and may offer the potential to realize both organizational and patient care benefits.
ANN Algorithms for Parkinson's, ALS, Huntington, and Healthy Walking Detection
In this study, the utilization of artificial neural networks (ANN) algorithms, in the diagnosis of neurodegenerative diseases were examined. Data obtained from the measurement of walking parameters were evaluated for disease diagnosis using the ANN model among individuals with ALS, Parkinson's, Huntington's, and healthy individuals. Comparative analyses conducted using Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms demonstrate that the Levenberg-Marquardt algorithm provides the most effective diagnosis with a success rate of 99%. This study highlights the potential of artificial neural networks in the early diagnosis of neurodegenerative diseases and lays a foundation for future research. In conclusion, artificial neural networks may play a significant role in the diagnosis of neurodegenerative diseases, but further research and method development in this area are warranted.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Machine Learning Interpretability in Diabetes Risk Assessment: A SHAP Analysis
Diabetes continues to be a complicated and prevalent metabolic illness, providing a serious burden to public health. While machine learning approaches like extreme gradient boosting (XGBoost) provide intriguing options for diabetes prediction, their 'black-box' nature typically limits clinical interpretability. To overcome this gap, our work applied SHapley Additive exPlanations (SHAP) to give insights into the XGBoost model's predictions. The dataset utilized in this research comprised of 253,680 patients and contained 21 parameters, such as General Health Status, High Blood Pressure Status, Age, and Body Mass Index. After feature selection using Recursive Feature Elimination (RFE), 15 important characteristics were discovered. In the test set, the XGBoost model obtained an accuracy of 86.6%, precision of 54.1%, recall of 17.0%, and an F1-score of 25.9% for the Original dataset. For the RFE dataset, the model displayed an accuracy of 86.6%, precision of 54.9%, recall of 16.5%, and an F1-score of 25.3%. SHAP analysis found that General Health Status, High Blood Pressure Status, Age, and Body Mass Index were the most important characteristics in both the Original and RFE datasets. This work provides as a platform for transparent and clinically applicable predictive modeling, assisting in early diabetes identification and preventive healthcare.
Bibliometric Analysis of Publications on Action Recognition, Convolutional Neural Network, Video Surveillance During 2011-2021
Action recognition based on convolutional neural networks (AR-CNN) has been developing rapidly in recent years. It is of great significance to conduct a deep analysis to understand the recent development of AR-CNN. However, a limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the publications related to the SciVal topic "Action Recognition; Convolutional Neural Network; Video Surveillance (T.561)" in computer vision research. This study focused on six aspects: literature distribution characteristics analysis, the development trend, citation analysis, collaborative analysis, keyword analysis and thematic evolution, using VOSviewer and Bibliometrix. The relevant publications were retrieved from Scopus in the period 2012–2021. A total of 6633 publications were identified by 9088 different authors, 62% were conference papers, and 35% were research articles. China and the USA contributed 39.7 % and 17.9 % of the total publications, respectively. The authors' production according to Price's Law was inconsistent but consistent according to Lotka's Law. Ling Shao was the most productive author, with 48 papers (%0.7). Chinese Academy of Sciences was the most productive affiliation, with 259 papers (%3.9). The first Bradford site consisted of Computer Science Lecture Notes with 617 publications. A moderately significant correlation was revealed between the country's publications and GDP per capita. The overall results show that the number of AR-CNN-related documents has increased significantly in recent years, with rapid growth from 2016. Although publications on AR-CNN were published mainly in European journals, China led the scientific production.