Computer methods and programs in biomedicine update最新文献

{"title":"Picture: A web application for decision support in glioma surgery","authors":"Maisa N.G. van Genderen ,&nbsp;Raymond M. Martens ,&nbsp;Frederik Barkhof ,&nbsp;Philip C. de Witt Hamer ,&nbsp;Roelant S. Eijgelaar","doi":"10.1016/j.cmpbup.2025.100199","DOIUrl":"10.1016/j.cmpbup.2025.100199","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Patients with glioma, the most common primary malignant brain tumor, often undergo surgery, aiming to remove as much tumor as possible while maintaining functional integrity. However, there is large variation in surgical decisions. This study aims to provide a data-driven approach to surgery planning and evaluation, estimating personalized potential extent of resection, based on a large multicenter MRI database.</div></div><div><h3>Methods</h3><div>We developed an interactive web-application (PICTURE tool), that uses segmented MRI scans from prior surgeries to create resection probability maps. The maps depict the chance of tumor tissue resection based on decisions in prior surgeries.</div></div><div><h3>Results</h3><div>The PICTURE tool enables uploading scans of a new patient and comparing these with the resection probability map of previous patients. This map can then be filtered for clinical characteristics to compare with similar patients and can be interactively explored to determine which parts of the tumor are more or less likely to be resected in a particular patient. Additionally, tumor characteristics and expected extent of resection are reported.</div></div><div><h3>Conclusions</h3><div>The PICTURE tool can enable data-driven glioma surgery planning through interactive generation of resection probability maps.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving brain tumor detection using FPGA-accelerated deep learning on Kria KV260 for smart healthcare","authors":"Kusum Lata ,&nbsp;Prashant Singh ,&nbsp;Sandeep Saini ,&nbsp;Linga Reddy Cenkeramaddi","doi":"10.1016/j.cmpbup.2025.100205","DOIUrl":"10.1016/j.cmpbup.2025.100205","url":null,"abstract":"<div><div>Technological advancements in high-performance electronics have fueled the development of cutting-edge medical applications, leading to exponential growth in effective treatment and diagnostic solutions for various medical problems. Incorporating deep learning-based systems with medical imaging technologies has revolutionized the field of disease detection. Ensuring the security and privacy of patient’s health records is crucial to developing sophisticated medical imaging diagnostic applications. This paper presents a privacy-focused, vision-based approach for effective brain tumor detection using deep learning algorithms such as ResNet-18, ResNet-50, and InceptionV3, deployed on the KV260 board, which is based on Xilinx® Kria™ K26 System on Module (SOM) platform, a Zynq® UltraScale+ MPSoC. We have integrated the AES-128 cryptographic algorithm with the Password-Based Key Derivation Function 2 (PBKDF2) hashing algorithm to maintain patients' privacy in MRI scans. This ensures the protection of patient data on the server and data movement to and from external servers. The designed system is evaluated for performance by examining its technical metric parameters- accuracy, precision, F1 score, and Recall. Security parameters such as entropy, energy, contrast, and correlation are used to evaluate the security strength of the proposed system. Microsoft operating systems compatible web application is also developed while integrating the above-proposed system on the KV 260 FPGA board. This application can be used remotely to upload the MRI scans and get the prediction results quickly and accurately. Performance assessment shows that ResNet18 outperforms testing-related metric parameters and execution time on the KV260 FPGA board while keeping patient data confidential, making it an ideal edge-device implementation for real-time clinical use.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics and optimal control of fractional-order monkeypox epidemic model with social distancing habits and public awareness","authors":"Raqqasyi Rahmatullah Musafir,&nbsp;Agus Suryanto,&nbsp;Isnani Darti,&nbsp;Trisilowati","doi":"10.1016/j.cmpbup.2025.100187","DOIUrl":"10.1016/j.cmpbup.2025.100187","url":null,"abstract":"<div><div>In this article, we propose a fractional-order monkeypox epidemic model incorporating social distancing habits and public awareness. The model includes the addition of a protected compartment and a saturated transmission rate. We implement a power rescaling for the parameters of the proposed model to ensure dimensional consistency. We have investigated the existence, uniqueness, nonnegativity, and boundedness of the solution. The model features monkeypox-free, human-endemic, and endemic equilibrium points, which depend on the order of derivative. The existence and stability of each equilibrium point have been analyzed locally and globally, depending on the basic reproduction number. Moreover, the basic reproduction number of the model also depends on the order of derivative. We carried out a case study using real data showing that the fractional-order model performs better than the first-order model in calibration and forecasting. Numerical simulations confirm the stability properties of each equilibrium point with respect to the specified parameter values. Numerical simulations also demonstrate that the social distancing habits can reduce monkeypox cases in the early stages, but do not significantly alter the basic reproduction number. Meanwhile, public awareness can substantially modify the basic reproduction number, shifting the endemic condition towards a disease-free state, although its impact on case reduction in the early period is not significant. We also implemented optimal control strategies for vector culling and vaccination in the proposed model. We have solved the optimal control problem, and the simulation results show that the combination of both controls yields the minimum cost with better effectiveness compared to the controls implemented separately.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects","authors":"Domenico Lofù ,&nbsp;Paolo Sorino ,&nbsp;Tommaso Colafiglio ,&nbsp;Caterina Bonfiglio ,&nbsp;Rossella Donghia ,&nbsp;Gianluigi Giannelli ,&nbsp;Angela Lombardi ,&nbsp;Tommaso Di Noia ,&nbsp;Eugenio Di Sciascio ,&nbsp;Fedelucio Narducci","doi":"10.1016/j.cmpbup.2024.100176","DOIUrl":"10.1016/j.cmpbup.2024.100176","url":null,"abstract":"<div><div>Metabolic dysfunction-associated fatty liver disease (MAFLD) introduces new diagnostic criteria for fatty liver disease that are independent of alcohol consumption and viral hepatitis infection. Therefore, investigating how biochemical and anthropometric factors influence mortality in MAFLD subjects is of significant interest. In this work, we propose MORIX, an Artificial Intelligence-based framework capable of predicting fatal mortality outcomes in subjects with MAFLD. MORIX utilizes data from epidemiological datasets containing carefully selected anthropometric and biochemical information. This selection is achieved through Recursive Feature Elimination (RFE) using a Random Forest (RF) to train Machine Learning (ML) algorithms and provide a mortality risk (Yes/No) output. To provide physicians with a valuable tool, MORIX was trained and tested on a dataset of MAFLD subjects, comparing five different models: Random Forest (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Light Gradient Boosting Model (LGBM) in a 5-fold cross-validation training strategy. Experimental results identified the RF as the best model, achieving a high accuracy for both mortality risks predicted. Additionally, an eXplainable Artificial Intelligence (XAI) analysis was conducted to clarify the diagnostic logic of the RF model and to assess the impact of each feature to the prediction. Moreover, a web application was developed to predict mortality risk and provide explanations of how the input features influenced the final prediction. In conclusion, the MORIX framework is easy to apply, and the required parameters are readily available in healthcare datasets, making it a practical tool for medical professionals.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100176"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart product service systems for remote patient monitoring under uncertainty: A hierarchical framework from a healthcare provider perspective","authors":"Yeneneh Tamirat Negash ,&nbsp;Faradilah Hanum ,&nbsp;Liria Salome Calahorrano Sarmiento","doi":"10.1016/j.cmpbup.2024.100174","DOIUrl":"10.1016/j.cmpbup.2024.100174","url":null,"abstract":"<div><h3>Background</h3><div>This study contributes to the integration of smart product service systems (smart PSSs) for remote patient monitoring (RPM). Integrating smart PSSs into RPM improves service delivery by enabling personalized care plans and shaping a patient-centered workflow for intelligent RPM. However, a gap exists in identifying intelligent RPM attributes and understanding their interrelationships. In addition, prior studies of RPM have yielded mixed results, with some studies demonstrating positive impacts and others showing no effect or even negative consequences on patient health. This inconsistency highlights the need for further investigation into how RPM systems are designed and utilized.</div></div><div><h3>Objectives</h3><div>First, the proposed intelligent RPM development criteria are validated through a qualitative assessment. Second, the interrelationships among intelligent RPM attributes are analyzed. Finally, the driving factors of intelligent RPM development are identified.</div></div><div><h3>Methods</h3><div>A hybrid methodology that combines the fuzzy Delphi method (FDM), the fuzzy decision-making trial and evaluation laboratory (FDEMATEL), and an analytical network process (ANP) is introduced to establish a hierarchical model of intelligent RPM attributes. Thirty healthcare industry experts specializing in chronic disease management participated in the study. Linguistic variables were utilized to manage the uncertainty inherent in expert opinions.</div></div><div><h3>Results</h3><div>The cause group encompassed operational efficiency, enhanced analytics, and sustainable service management, whereas the effect group comprised patient satisfaction and platform technology. The driving criteria included personalized treatment plans, real-time monitoring, mobile app development, and accessibility.</div></div><div><h3>Conclusion</h3><div>This study advances the understanding of how smart PSSs can be integrated into healthcare delivery. The developed hierarchical framework provides a roadmap for healthcare providers to implement and optimize intelligent RPM systems.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100174"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis","authors":"Md. Alif Sheakh ,&nbsp;Sami Azam ,&nbsp;Mst. Sazia Tahosin ,&nbsp;Asif Karim ,&nbsp;Sidratul Montaha ,&nbsp;Kayes Uddin Fahim ,&nbsp;Niusha Shafiabady ,&nbsp;Mirjam Jonkman ,&nbsp;Friso De Boer","doi":"10.1016/j.cmpbup.2025.100181","DOIUrl":"10.1016/j.cmpbup.2025.100181","url":null,"abstract":"<div><div>Endometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are employed to increase the quality of the images, including normalization, Non-Local Means denoising, and alpha-beta enhancement. Effective segmentation is achieved through a combination of Otsu thresholding, morphological operations, distance transformations, and the watershed approach to identify major regions of interest. Through a sequence of blocks, the ECgMLP architecture processes input images to remove unimportant patterns. Model hyperparameters are improved via ablation research. The evaluations show a maximum accuracy of 99.26 % for identifying multi-class histopathological categories of endometrial tissue, which is higher than the previous best technique. The proposed model offers an automated, correct diagnosis, enhancing clinical processes. This proposition could be added to the current tools for finding endometrial cancer early, leading to better patient outcomes.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Caputo fractional-order model with MCMC for rabies transmission dynamics","authors":"Jufren Zakayo Ndendya ,&nbsp;Joshua A. Mwasunda ,&nbsp;Stephen Edward ,&nbsp;Nyimvua Shaban Mbare","doi":"10.1016/j.cmpbup.2025.100206","DOIUrl":"10.1016/j.cmpbup.2025.100206","url":null,"abstract":"<div><div>Rabies continues to pose a severe public health threat, particularly in regions with high interactions between humans and infected dog populations. This study develops a fractional-order mathematical model using the Caputo derivative to capture the memory and hereditary effects in rabies transmission dynamics. The model incorporates key intervention strategies, including public health education, treatment, and culling of stray and infected dogs, to evaluate their effectiveness in controlling rabies outbreaks. The Markov Chain Monte Carlo (MCMC) method is utilized for parameter estimation, enhancing model precision and predictive accuracy. Stability analysis demonstrates that the disease-free equilibrium is locally asymptotically stable when effective reproduction number <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub><mo>&lt;</mo><mn>1</mn></mrow></math></span>. Numerical simulations reveal that fractional-order model provides a more flexible and realistic representation of rabies spread compared to classical integer-order model. The results highlight the significant impact of public health education, treatment and targeted culling in reducing infection rates. The findings offer crucial insights for policymakers and public health officials in designing optimal intervention strategies to achieve sustainable rabies control.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of preprocessing for Generative Adversarial Networks: A case study on color fundoscopy to fluorescein angiography image-to-image translation","authors":"Veena K.M. ,&nbsp;Veena Mayya ,&nbsp;Rashmi Naveen Raj ,&nbsp;Sulatha V. Bhandary ,&nbsp;Uma Kulkarni","doi":"10.1016/j.cmpbup.2025.100179","DOIUrl":"10.1016/j.cmpbup.2025.100179","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) are capturing the attention of peer researchers in paired or unpaired image-to-image translation applications, particularly in the domain of retinal image processing. Additionally, there are several effective image preprocessing techniques available that can significantly improve the performance of GANs. This study examines the impact of five different image preprocessing techniques - Green Channel, CLAHE on Green Channel, CLAHE on RGB channels, Green Channel Gaussian Convolution, and RGB Gaussian Convolution - on five different GAN variants: CycleGAN, Pix2Pix GAN, CUT GAN, FastCut GAN, and NICE GAN. The study involved conducting 30 experiments to assess the performances of these GAN variants in the image-to-image translation of dual-mode retinal images. The evaluation utilized Frechet Inception Distance (FID) and Kernel Inception Distance (KID) metric scores to measure the performance of the GAN variants. The results demonstrated that the CycleGAN model achieved the best performance with CLAHE on RGB preprocessed images, achieving the lowest FID and KID scores of 103.49 and 0.038, respectively. This investigation underscores the significant potential of image preprocessing techniques in enhancing the performance of GANs in image translation applications.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis","authors":"Behnaz Motamedi,&nbsp;Balázs Villányi","doi":"10.1016/j.cmpbup.2025.100184","DOIUrl":"10.1016/j.cmpbup.2025.100184","url":null,"abstract":"<div><div>Effective disease management necessitates the accurate and timely prediction of lung cancer and diabetes. Machine learning (ML) based models have garnered attention in the realm of predictive healthcare, with ensemble methods, in particular, bolstering algorithms to improve classification performance. Nevertheless, enhancing boosting algorithms to achieve superior predictive accuracy continues to be a difficult task. This study proposes a Bayesian-Optimized GentleBoost Ensemble (BOGBEnsemble) to improve classification performance for diabetes prediction (DiP) and lung cancer prediction (LCP). Two Kaggle datasets—a diabetes dataset from multiple healthcare providers and a Survey Lung Cancer dataset from existent medical records—are utilized. Data preprocessing involves outlier removal, min–max normalization, class balancing, and Pearson correlation-based feature selection. The GentleBoost classifier is optimized using Bayesian hyperparameter tuning, focusing on learning rate and the number of weak learners, and is validated using 10-fold cross-validation. BOGBEnsemble is evaluated in comparison to leading models, such as Random Forest (RF), Adaptive Boosting (AdaBoost), Logistic Boosting (LogitBoost), Random Undersampling Boosting (RUSBoost), conventional GentleBoost, and Multi-Layer Perceptron (MLP) architectures. The DiP-BOGBEnsemble achieves a 99.26% accuracy, 98.94% precision, 99.60% recall, 99.26% F1-score, 99.46% F2-score, 98.51% MCC, 98.51 Kappa, 0.0041 FOR, and 22,606.75 DOR. The LC-BOGBEnsemble achieves a 96.51% accuracy, 97.83% precision, 94.76% recall, 96.28% F1-score, 95.36% F2-score, MCC of 93.03%, Kappa of 92.99, FOR of 0.0462, and DOR of 932.15. This study highlights the potential of BOGBEnsemble as a clinically viable tool for early disease detection and decision support, paving the way for more reliable and personalized healthcare strategies.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks","authors":"Seyed Alireza Khanghahi ,&nbsp;Hadi Kamkar ,&nbsp;Seyedehsamaneh Shojaeilangari ,&nbsp;Abdollah Allahverdi ,&nbsp;Parviz Abdolmaleki","doi":"10.1016/j.cmpbup.2025.100208","DOIUrl":"10.1016/j.cmpbup.2025.100208","url":null,"abstract":"<div><div>Inhibiting the mammalian Target of Rapamycin (mTOR) represents a promising strategy in cancer therapy due to its crucial role in cell growth, survival, and metabolism. Using a variety of quantitative structure-activity relationship (QSAR) models, we present a comprehensive comparison of deep learning (DL) and classical machine learning (ML) techniques for modeling mTOR inhibitor activity. Unlike prior studies that focused on specific algorithms or limited descriptors, we benchmark a wide range of models, from traditional models like Random Forest, logistic regression, and SVM to modern algorithms like CNNs, GRUs, and LSTMs, on both descriptor-based features obtained by Dragon and descriptor-free inputs, including raw SMILES string, and Morgan fingerprints. This comprehensive analysis provides a robust foundation for using the best QSAR models specific to mTOR inhibition. Our findings revealed that while the Random Forest classifier achieved the highest accuracy among all models (0.9290 accuracy, 0.8940 F1-score, 0.9737 AUC), DL methods also demonstrated strong predictive capabilities, with nearly all models attaining an accuracy above 0.90. Among the DL models, CNN-QSAR using Morgan fingerprints achieved the highest accuracy (0.9271), F1-score (0.8950), and AUC (0.9696), demonstrating its effectiveness in capturing structural characteristics. The GRU-QSAR and LSTM-QSAR models, which utilized tokenized SMILES, achieved accuracies of 0.9002 and 0.9021, F1-scores of 0.8595 and 0.8603, and AUCs of 0.9270 and 0.9529, respectively, leveraging their ability to process sequential data.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}