Computer methods and programs in biomedicine update最新文献

{"title":"An analytical framework for smoking epidemic modeling using fuzzy logic and dual time-delay dynamics","authors":"Muhammad Tashfeen ,&nbsp;Hothefa Shaker Jassim ,&nbsp;Muhammad Aziz ur Rehman ,&nbsp;Fazal Dayan ,&nbsp;Muhammad Adil Sadiq ,&nbsp;Husam A. Neamah","doi":"10.1016/j.cmpbup.2025.100218","DOIUrl":"10.1016/j.cmpbup.2025.100218","url":null,"abstract":"<div><div>The process of smoking is divided into several stages and has a clear tendency towards uncertainty and variability, which are not reflected in the traditional models with presumed parameters. To overcome this difficulty, a fuzzy mathematical model is derived to represent smoking dynamics more accurately under uncertainty. The PSRQE model presented and comprises Potential, Social, Regular, Transitional Non-smokers, and Ex-smokers, integrates vital considerations like the chance of developing smoking and the chance of quitting smoking. The model is analyzed by a stability analysis, numerical simulations, and sensitivity analysis of the basic reproduction number <span><math><msub><mi>R</mi><mi>o</mi></msub></math></span>. Three algorithms based on the Forward Euler scheme, the Fourth-Order Runge-Kutta (RK-4) treatment method, and the Non-Standard Finite Difference (NSFD) technique are used to obtain numerical solutions. The NSFD scheme is positive and bounded by convergence analysis, and simulation results have shown that it also preserves the structural properties of the model even when the step sizes are larger. Moreover, the influence of time deviations <span><math><mrow><msub><mi>τ</mi><mn>1</mn></msub><mspace></mspace></mrow></math></span>and <span><math><msub><mi>τ</mi><mn>2</mn></msub></math></span> on the smoking habits is also examined. It is demonstrated that this framework provides a valuable foundation for comprehending the leading patterns that govern smoking behavior that are required to reduce smoking rates and the related social, health, and economic impacts.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100218"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104731","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":"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":"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":"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":"U-net based approach for pectoralis muscle segmentation in digital mammography","authors":"Francesca Angelone ,&nbsp;Alfonso Maria Ponsiglione ,&nbsp;Roberto Grassi ,&nbsp;Francesco Amato ,&nbsp;Mario Sansone","doi":"10.1016/j.cmpbup.2025.100210","DOIUrl":"10.1016/j.cmpbup.2025.100210","url":null,"abstract":"<div><div>Accurate segmentation of the breast is a fundamental step in computer-aided diagnosis (CAD) systems for mammography. In particular, several tasks, such as the classification of breast density, evaluation of correct positioning of the breast, and automatic detection and classification of suspicious lesions, preliminarily require an accurate segmentation of the pectoralis muscle. This study aims to propose an automatic breast segmentation algorithm that combines traditional methods with Deep Learning methods limited only to the border region between the muscle and the breast. This type of approach allows for reducing the risk of having good overall accuracy in multi-class classification that does not reflect adequate accuracy with respect to small classes, such as the pectoralis muscle in a mammographic image. The U-Net network was therefore implemented on patches extracted along the straight line with which the muscle-breast edge was first estimated. The predicted patches are repositioned to perform an edge refinement and obtain the total breast mask, using histogram-based thresholding to segment the background from the breast. The results show Dice values equal to 0.848 ± 0.196 and Jaccard index equal to 0.774 ± 0.227 for the single patches, and Dice values equal to 0.971 ± 0.011 and Jaccard index equal to 0.944 ± 0.022 for the entire breast segmentation.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770953","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":"A review and systematic guide to counteracting medical data scarcity for AI applications","authors":"Fabian Gröger ,&nbsp;Ludovic Amruthalingam ,&nbsp;Simone Lionetti ,&nbsp;Alexander A. Navarini ,&nbsp;Fabian Ille ,&nbsp;Marc Pouly","doi":"10.1016/j.cmpbup.2025.100220","DOIUrl":"10.1016/j.cmpbup.2025.100220","url":null,"abstract":"<div><div>Artificial intelligence has the potential to improve the scalability, objectivity, and precision of the overall healthcare system. Such improvements are possible due to the growth of medical databases and the progress of deep learning approaches, which enable automated analysis of both structured and unstructured data. While the overall size of medical datasets continues to increase, data scarcity remains problematic due to challenges in the medical domain, such as rare diseases, difficult and expensive annotation, and restricted population coverage. Machine learning models trained without appropriate measures to counteract this scarcity are often biased and unreliable in real-world settings. This paper will systematically examine the different challenges arising from medical data scarcity, their implications, and state-of-the-art mitigation approaches. It includes studies from the general machine learning community and describes how their findings translate to medical applications. This review is meant as a practical resource for researchers who want to develop reliable machine learning models for medical applications when data is scarce.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100220"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227202","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":"Electronic community health information system practice and associated factors among health extension workers in South Wollo Zone, North East Ethiopia: Mixed study","authors":"Mohammed Ali Dawud ,&nbsp;Mulugeta Hayelom Kalayou ,&nbsp;Yitbarek Wasihun ,&nbsp;Toyeb Yasine ,&nbsp;Tewoflos Ayalew ,&nbsp;Mulugeta Desalegn Kasaye","doi":"10.1016/j.cmpbup.2025.100215","DOIUrl":"10.1016/j.cmpbup.2025.100215","url":null,"abstract":"<div><h3>Background</h3><div>Despite several hindering factors, such as limited internet access, unstable power supply, insufficient smart phone, lack of trainings regarding e-CHIS, affecting its implementation, electronic community health information system is a digitized type of community health information system content on a mobile platform that creates a logically interconnected programmatic content module for usage by health extension workers to register and provide high quality health services across the Nation. This study assessed electronic community health information system practice and its associated factors among health extension workers of south Wollo zone, Amhara, Ethiopia, 2024.</div></div><div><h3>Methods</h3><div>A facility-based cross-sectional study was conducted from 22 January 2024 to 01 April 2024. Study participants were selected by using simple random sampling for the quantitative study, and purposive sampling was employed for qualitative study. Data were collected using an interviewer-administered questionnaire. Data were entered into Epi Data version 4.6.1 and exported to SPSS version 26 for analysis. Descriptive statistics were summarized using figures and tables. Both bi-variable and multivariable logistic regression analyses were carried out. The level of significance was determined based on the AOR with 95 % CI and P-value at &lt;0.05. Thematic analysis was used to analyze the data for qualitative part.</div></div><div><h3>Results</h3><div>In this study, 46 % of health extension workers showed Good practice of eCHIS. Respondents’ Knowledge, presence of electricity at the health facility, Availability of tablets for eCHIS, and work experience of participants were statistically significant associations with the practice of eCHIS.</div></div><div><h3>Conclusion and Recommendation</h3><div>In this study, the practice of eCHIS was 46 %. Variables such as availability of tablets, work experience, knowledge, and facility electricity supply were factors associated with electronic-health-information-system. Improving the knowledge of the participants would improve the e-CHIS practice.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750274","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":"Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data","authors":"Shehu Mohammed,&nbsp;Neha Malhotra","doi":"10.1016/j.cmpbup.2025.100209","DOIUrl":"10.1016/j.cmpbup.2025.100209","url":null,"abstract":"<div><div>Alzheimer’s Disease (AD) is a significant global health issue, and the current diagnostic techniques cannot diagnose the disease at its early stages, hence the difficulty of early therapeutic management. In response to the formulated research problem, this study articulates a new multimodal machine-learning framework for early AD diagnosis. The main goal is to combine multiple biomarkers: neuroimaging, CSF, genetic, and longitudinal cognitive data and develop a robust model for accurate early AD diagnosis. The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. The methodology involves federated learning and domain adaptation with GANs to integrate data from multiple centers with the patient’s privacy intact. It shows that the proposed multimodal model is superior to single-modality models with an AUC-ROC of 0.94 and reveals that hippocampal volume and plasma p-tau are the most informative biomarkers in the early diagnosis of AD. The study’s implications indicate that combining multimodal data improves diagnostic accuracy and clinical relevance by providing a roadmap to developing personalized medicine and better patient care. Future work will be aimed at increasing the variability of the dataset and the clinical trials to test the model to improve its applicability and performance in actual practice.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750303","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}