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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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.
Juxtaposition across Mathematical Modeling, Stochastic Processes along with the Lenses of Philosophy of Science and AI integration in Medicine and Biology: An Overview
The ultimate reason for the ubiquity of mathematics in modern science entails the essence of mathematical thinking and processes so that complex phenomena including those emerging in medical and biological systems can be understood, and thus, scientific models at their crux can be generated. The consequent complexities and uncertainties require the applications of stochastic processes in mathematical modeling with Artificial Intelligence (AI) techniques used in realms of medicine and biology. Within these conditions, clinical evaluation evidence and model explainability are considered to ensure accountable, effective and safe uses of AI in clinical settings, along with robust, reliable as well as accurate understanding of various complex processes that manifest huge numbers of heterogeneous temporospatial scales. The role of philosophy within science can be expounded by its juxtaposition with models and empirical data explicated by philosophy whose pillars are driven into semantic, pragmatic and syntactic structures of scientific theory that also make up the foundational tenets of algorithmic thinking and patterns. While philosophy of science examines and reflects on the concepts, theories, arguments and methods of science, it should also be borne in mind that scientific theory, by its definition, relates to applications validated by its predictions as units of analyses. Concerning mathematical models, their structure and behavior in target systems are also to be addressed so that explicit common patterns that are implicit in scientific practice can be included in this complex influx. On the other hand, critical functions of mathematical modeling from the pragmatic aspect include the unification of models and data, model fitting to the data, identification of mechanisms depending on observations as well as predictions of future observations. Given these, philosophy of science in medical and biological fields is stated to prompt a comprehensive understanding to construct holistic mathematical models particularly in complex sciences including different attributes of complexity, evolution and adaptation. Regarding the position of AI, its algorithms and mathematical modeling, the methods of neural networks, statistics, operations research, fractional calculus, fractals, and so forth can be employed with AI being capable of uncovering hidden insights embedded in big data concerning medical and biological issues in view of contemporary scientific thinking and processes. In addition, the treatment and handling of uncertainty in clinical medicine and biological problems over their processes may disclose compelling challenges due the fact that uncertainties are one of the intrinsic features of nearly all mathematical models which are formed based on three basic types of uncertainty: interval, Bayesian and stochastic. Accordingly, the current overview aims at providing answers built on sophisticated models considering the explanation and interpretation of design and formulation considering that the extant research literature could have some fragmented points in terms of original and application-oriented works. To these ends, the opportunities, challenges, limitations and conjunctures with respect to mathematical modeling in medicine and biology are addressed while role of philosophy of science is discussed within the context of mathematical modeling and applications in medicine and biology. In addition to these points, the delineation of forecasting, prediction, estimation and approximation concerning different mathematical modeling with the integration of AI in medicine and biology is explained. Thereby, an overview is inclusively presented by comprising the principles underpinning the medical and biological systems within a framework in relation to the diagnostic and disease-related treatment processes and follow-up, which can provide new directions in novel formulations, designs and interpretations based on mathematical modeling processes to be constructed and solved through practicality as well as to-the-point specific means.
Computational fractalbased methodologies in medicine and biology
Engineering applications in medicine and biology
Computational fractal based methodologies in medicine and biology
Examination and Evaluation of Obesity Risk Factors with Explainable Artificial Intelligence
There is an increasing need for effective methods for the detection and management of obesity, which is an important public health problem worldwide and is critical for the sustainability of health systems. This study examines the effectiveness of data preprocessing and machine learning techniques in detecting obesity. Data preprocessing steps, including the removal of unnecessary data, handling missing values, and addressing data imbalance, are necessary to enhance the accuracy of machine learning algorithms. In this study, data preprocessing steps were applied to an obesity dataset to make it suitable for machine learning. Using a dataset of 2111 patients, this study evaluates the effectiveness of machine learning techniques in detecting obesity. Following the completion of data preprocessing, obesity was identified using various machine learning algorithms, including Decision Tree Classification, Random Forest Classification, Naive Bayes Classification, KNN, and XGBoosting, and their performances were compared. According to the results, the XGBoosting algorithm exhibited the highest accuracy (0.92), precision, recall, and F1-score values. Explainable Artificial Intelligence (XAI) techniques, such as SHAP and InterpretML, were employed to understand the effects of obesity parameters and determine which parameters have a greater impact on obesity. Through visualizing and analyzing the effects of obesity parameters, these techniques facilitated the identification of significant parameters in obesity detection. The findings demonstrate that the XGBoosting algorithm outperforms other algorithms in detecting obesity. Furthermore, XAI techniques play a crucial role in comprehending obesity parameters. Specifically, a family history of obesity and factors like FCVC and CAEC appear to have more significant effects compared to others.