Computer methods and programs in biomedicine最新文献

{"title":"ConnectomeAE: Multimodal brain connectome-based dual-branch autoencoder and its application in the diagnosis of brain diseases","authors":"Qiang Zheng ,&nbsp;Pengzhi Nan ,&nbsp;Yongchao Cui ,&nbsp;Lin Li","doi":"10.1016/j.cmpb.2025.108801","DOIUrl":"10.1016/j.cmpb.2025.108801","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patients, ignoring important information about radiomics features such as shape and texture of individual brain regions in structural images. To this end, this study proposed a novel deep learning approach to integrate multimodal brain connectome information and regional radiomics features for brain disease diagnosis.</div></div><div><h3>Methods</h3><div>A dual-branch autoencoder (ConnectomeAE) based on multimodal brain connectomes was proposed for brain disease diagnosis. Specifically, a matrix of radiomics feature extracted from structural magnetic resonance image (MRI) was used as Rad_AE branch inputs for learning important brain region features. Functional brain network built from functional MRI image was used as inputs to Cycle_AE for capturing brain disease-related connections. By separately learning node features and connection features from multimodal brain networks, the method demonstrates strong adaptability in diagnosing different brain diseases.</div></div><div><h3>Results</h3><div>ConnectomeAE was validated on two publicly available datasets. The experimental results show that ConnectomeAE achieved excellent diagnostic performance with an accuracy of 70.7 % for autism spectrum disorder and 90.5 % for Alzheimer's disease. A comparison of training time with other methods indicated that ConnectomeAE exhibits simplicity and efficiency suitable for clinical applications. Furthermore, the interpretability analysis of the model aligned with previous studies, further supporting the biological basis of ConnectomeAE.</div></div><div><h3>Conclusions</h3><div>ConnectomeAE could effectively leverage the complementary information between multimodal brain connectomes for brain disease diagnosis. By separately learning radiomic node features and connectivity features, ConnectomeAE demonstrated good adaptability to different brain disease classification tasks.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108801"},"PeriodicalIF":4.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facial surgery preview based on the orthognathic treatment prediction","authors":"Huijun Han ,&nbsp;Congyi Zhang ,&nbsp;Lifeng Zhu ,&nbsp;Pradeep Singh ,&nbsp;Richard Tai-Chiu Hsung ,&nbsp;Yiu Yan Leung ,&nbsp;Taku Komura ,&nbsp;Wenping Wang ,&nbsp;Min Gu","doi":"10.1016/j.cmpb.2025.108781","DOIUrl":"10.1016/j.cmpb.2025.108781","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Orthognathic surgery consultations are essential for helping patients understand how their facial appearance may change after surgery. However, current visualization methods are often inefficient and inaccurate due to limited pre- and post-treatment data and the complexity of the treatment. This study aims to develop a fully automated pipeline for generating accurate and efficient 3D previews of postsurgical facial appearances without requiring additional medical images.</div></div><div><h3>Methods:</h3><div>The proposed method incorporates novel aesthetic criteria, such as mouth-convexity and asymmetry, to improve prediction accuracy. To address data limitations, a robust data augmentation scheme is implemented. Performance is evaluated against state-of-the-art methods using Chamfer distance and Hausdorff distance metrics. Additionally, a user study involving medical professionals and engineers was conducted to evaluate the effectiveness of the predicted models. Participants performed blinded comparisons of machine learning-generated faces and real surgical outcomes, with McNemar’s test used to analyze the robustness of their differentiation.</div></div><div><h3>Results:</h3><div>Quantitative evaluations showed high prediction accuracy for our method, with a Hausdorff Distance of 9.00 millimeters and Chamfer Distance of 2.50 millimeters, outperforming the state of the art. Even without additional synthesized data, our method achieved competitive results (Hausdorff Distance: 9.43 millimeters, Chamfer Distance: 2.94 millimeters). Qualitative results demonstrated accurate facial predictions. The analysis revealed slightly higher sensitivity (54.20% compared to 53.30%) and precision (50.20% compared to 49.40%) for engineers compared to medical professionals, though both groups had low specificity, approximately 46%. Statistical tests showed no significant difference in distinguishing Machine Learning-Generated faces from Real Surgical Outcomes, with p-values of 0.567 and 0.256, respectively. Ablation tests demonstrated the contribution of our loss functions and data augmentation in enhancing prediction accuracy.</div></div><div><h3>Conclusion:</h3><div>This study provides a practical and effective solution for orthognathic surgery consultations, benefiting both doctors and patients by improving the efficiency and accuracy of 3D postsurgical facial appearance previews. The proposed method has the potential for practical application in pre-surgical visualization and aiding in decision-making.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108781"},"PeriodicalIF":4.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FADE: Forecasting for anomaly detection on ECG","authors":"Paula Ruiz-Barroso ,&nbsp;Francisco M. Castro ,&nbsp;José Miranda ,&nbsp;Denisa-Andreea Constantinescu ,&nbsp;David Atienza ,&nbsp;Nicolás Guil","doi":"10.1016/j.cmpb.2025.108780","DOIUrl":"10.1016/j.cmpb.2025.108780","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple approaches have been proposed in the literature to address the challenge of detecting ECG anomalies. Typically, these methods are based on the manual interpretation of ECG signals, which is time consuming and depends on the expertise of healthcare professionals. The objective of this work is to propose a deep learning system, FADE, designed for normal ECG forecasting and anomaly detection, which reduces the need for extensive labeled datasets and manual interpretation.</div></div><div><h3>Methods:</h3><div>We propose FADE, a deep learning system designed for normal ECG forecasting, trained in a self-supervised manner with a novel morphological inspired loss function, that can be used for anomaly detection. Unlike conventional models that learn from labeled anomalous ECG waveforms, our approach predicts the future of normal ECG signals, thus avoiding the need for extensive labeled datasets. Using a novel distance function to compare forecasted ECG signals with actual sensor data, our method effectively identifies cardiac anomalies. Additionally, this approach can be adapted to new contexts (e.g., different sensors, patients, etc.) through domain adaptation techniques. To evaluate our proposal, we performed a set of experiments using two publicly available datasets: MIT-BIH NSR and MIT-BIH Arrythmia.</div></div><div><h3>Results:</h3><div>The results demonstrate that our system achieves an average accuracy of 83.84% in anomaly detection, while correctly classifying normal ECG signals with an accuracy of 85.46%.</div></div><div><h3>Conclusions:</h3><div>Our proposed approach exhibited superior performance in the early detection of cardiac anomalies in ECG signals, surpassing previous methods that predominantly identify a limited range of anomalies. FADE effectively detects both abnormal heartbeats and arrhythmias, offering significant advantages in healthcare through cost reduction, facilitation of remote monitoring, and efficient processing of large-scale ECG data.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108780"},"PeriodicalIF":4.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling of cardiac biventricular electromechanics with coronary blood flow to investigate the influence of coronary arterial motion on coronary haemodynamic","authors":"Laila Fadhillah Ulta Delestri ,&nbsp;Amr Al Abed ,&nbsp;Socrates Dokos ,&nbsp;Mohd Jamil Mohamed Mokhtarudin ,&nbsp;Foo Ngai Kok ,&nbsp;Neil W Bressloff ,&nbsp;Bram G Sengers ,&nbsp;Azam Ahmad Bakir","doi":"10.1016/j.cmpb.2025.108800","DOIUrl":"10.1016/j.cmpb.2025.108800","url":null,"abstract":"<div><h3>Background and objective</h3><div>Coronary flow is strongly influenced by the geometry and motion of coronary arteries, which change periodically in response to myocardial contraction throughout the cardiac cycle. However, a computational framework integrating cardiac biventricular electromechanics with dynamic coronary artery flow using a simplified, yet comprehensive mathematical approach remains underexplored. This study aims to develop a coupled 3D model of cardiac biventricular electromechanics and coronary circulation, enabling simulation of the interplay between cardiac electrical activity, mechanical function and coronary flow.</div></div><div><h3>Methods</h3><div>A patient-specific biventricular electromechanical model encompasses the fibre orientation, electrophysiology, mechanical properties and an open-loop heart circulation is developed. The electromechanical model is simulated independently from the coronary circulation model. The model provides an input for the Navier-Stokes-based coronary flow model. A one-way coupling approach maps the biventricular motion to the coronary arteries, linking both components. To evaluate the influence of coronary arterial motion on coronary haemodynamic, simulations are performed for two scenarios: a moving and a non-moving (static) coronary artery model.</div></div><div><h3>Results</h3><div>Cardiac-induced coronary motion alters the pressure, velocity and flow profiles. Non-moving coronary arteries produce stable counter-rotating Dean-like vortices due to steady flow dominated by centrifugal forces, while the moving arteries disrupt these vortices as arterial curvature changes disturb the flow. Coronary motion significantly affects the wall shear stress, highlighting the necessity of incorporating arterial dynamics to investigate atherosclerosis.</div></div><div><h3>Conclusion</h3><div>The integrated biventricular-coronary model emphasizes the significance of background cardiac motion in coronary haemodynamic. The model offers a foundation for exploring myocardial perfusion mechanisms in realistic physiological settings.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108800"},"PeriodicalIF":4.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the impact of atrial fibrillation on the vascular onset of glaucoma via multiscale cardiovascular modeling","authors":"Stefania Scarsoglio ,&nbsp;Luca Congiu ,&nbsp;Luca Ridolfi","doi":"10.1016/j.cmpb.2025.108783","DOIUrl":"10.1016/j.cmpb.2025.108783","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Atrial fibrillation (AF) is the most common tachyarrhythmia, exhibiting faster and irregular beating. Although there is growing evidence of the impact of AF on the cerebral hemodynamics, ocular hemodynamic alterations induced by AF are still poorly investigated to date. The objective of this study is to computationally inquire into the role of AF on the ocular hemodynamics as one of the possible vascular triggers of glaucoma, which is the leading cause of blindness due to the damage of the optic nerve.</div></div><div><h3>Methods:</h3><div>A validated 0D–1D multiscale cardiovascular model is exploited to compute the hemodynamic response of AF against sinus rhythm (SR), by simulating 2000 beats for each condition. To mimic AF rhythm, its main features are accounted for: (i) accelerated, variable and uncorrelated beating; (ii) absence of atrial kick; (iii) ventricular systolic dysfunction.</div></div><div><h3>Results:</h3><div>We focused on intraocular pressure (<span><math><mrow><mi>I</mi><mi>O</mi><mi>P</mi></mrow></math></span>), ocular perfusion pressure (<span><math><mrow><mi>O</mi><mi>P</mi><mi>P</mi></mrow></math></span>), and translaminar pressure (<span><math><mrow><mi>T</mi><mi>L</mi><mi>P</mi></mrow></math></span>). Apart from a modest <span><math><mrow><mi>O</mi><mi>P</mi><mi>P</mi></mrow></math></span> decrease, beat-averaged values of <span><math><mrow><mi>I</mi><mi>O</mi><mi>P</mi></mrow></math></span> and <span><math><mrow><mi>T</mi><mi>L</mi><mi>P</mi></mrow></math></span> barely vary in AF with respect to SR. Instead, during AF a significant reduction and dispersion of pulsatile values (i.e., maximum minus minimum values reached in a beat), as well as wave amplitude damping, is observed for <span><math><mrow><mi>I</mi><mi>O</mi><mi>P</mi></mrow></math></span>, <span><math><mrow><mi>O</mi><mi>P</mi><mi>P</mi></mrow></math></span> and <span><math><mrow><mi>T</mi><mi>L</mi><mi>P</mi></mrow></math></span>. The marked variability of pulsatile values, which are hardly measured due to clinical difficulties, can induce transient hypoperfusions and hypo-pulsatility events (for <span><math><mrow><mi>O</mi><mi>P</mi><mi>P</mi></mrow></math></span>) as well as hypertensive episodes (for <span><math><mrow><mi>T</mi><mi>L</mi><mi>P</mi></mrow></math></span>).</div></div><div><h3>Conclusions:</h3><div>Awaiting necessary clinical data which are to date lacking, the present study can enrich – through hemodynamic-driven hints in the AF framework – the vascular theory, which associates reduced ocular perfusion (by means of decreased <span><math><mrow><mi>O</mi><mi>P</mi><mi>P</mi></mrow></math></span> and increased <span><math><mrow><mi>T</mi><mi>L</mi><mi>P</mi></mrow></math></span>) to an augmented risk of glaucoma. In this context, present modeling findings suggest a possible mechanistic link between AF-induced hemodynamic alterations and the increased risk of glaucoma development.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108783"},"PeriodicalIF":4.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hemodynamics affects factor XI/XII anticoagulation efficacy in patient-derived left atrial models","authors":"M. Guerrero-Hurtado ,&nbsp;M. Garćia-Villalba ,&nbsp;A. Gonzalo ,&nbsp;E. Durán ,&nbsp;P. Martinez-Legazpi ,&nbsp;P. Ávila ,&nbsp;A.M. Kahn ,&nbsp;M.Y. Chen ,&nbsp;E. McVeigh ,&nbsp;J. Bermejo ,&nbsp;J.C. del Álamo ,&nbsp;O. Flores","doi":"10.1016/j.cmpb.2025.108761","DOIUrl":"10.1016/j.cmpb.2025.108761","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Atrial fibrillation (AF) is a common arrhythmia that disrupts blood circulation in the left atrium (LA), causing stasis in the left atrial appendage (LAA) and increasing thromboembolic risk. In patients at sufficiently high risk, anticoagulation is indicated. This benefit may be counterbalanced by an increased risk of bleeding. Novel anticoagulants under development, such as factor XI/XII inhibitors, may be associated with a lower bleeding risk. However, their efficacy in preventing thrombosis is not fully understood. We hypothesized that patient-specific flow patterns in the LA and LAA not only influence the risk of thrombosis but also the effectiveness of anticoagulation agents.</div></div><div><h3>Methods:</h3><div>To test our hypothesis, we simulated blood flow and the intrinsic coagulation pathway in patient-specific LA anatomies with and without factor XI/XII inhibition. We included a heterogeneous cohort of thirteen patients, some in sinus rhythm and others in AF, four of whom had an LAA thrombus or a history of transient ischemic attacks. We used computational fluid dynamics based on 4D CT imaging and a detailed 32-coagulation factor system to run 247 simulations. We analyzed baseline LA flow patterns and evaluated various factor XI/XII inhibition levels. Implementing a novel multi-fidelity coagulation modeling approach accelerated computations by two orders of magnitude, enabling many simulations to be performed.</div></div><div><h3>Results:</h3><div>The simulations provided spatiotemporally resolved maps of thrombin concentration throughout the LA, showing that it peaks inside the LAA. Coagulation metrics based on peak LAA thrombin dynamics suggested patients could be classified as having no, moderate or high thromboembolic risk. High-risk patients had slower flows and higher residence times in the LAA than those with moderate thromboembolic risk, and they required stronger factor XI/XII inhibition to prevent thrombin growth. These data suggest that the anticoagulation effect was also related to the LAA hemodynamics.</div></div><div><h3>Conclusion:</h3><div>The methodology outlined in this study has the potential to enable personalized assessments of coagulation risk and to tailor anticoagulation therapy by analyzing flow dynamics in patient-derived LA models, representing a significant step towards advancing the application of digital twins in cardiovascular medicine.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108761"},"PeriodicalIF":4.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin","authors":"Martin Manninger ,&nbsp;Ingmar Lercher ,&nbsp;Astrid N.L. Hermans ,&nbsp;Jonas L. Isaksen ,&nbsp;Anton J. Prassl ,&nbsp;Andreas Zirlik ,&nbsp;Kevin Vernooy ,&nbsp;Sevasti-Maria Chaldoupi ,&nbsp;Justin Luermans ,&nbsp;Rachel M.A. ter Bekke ,&nbsp;Jørgen K. Kanters ,&nbsp;Gernot Plank ,&nbsp;Daniel Scherr ,&nbsp;Thomas Pock ,&nbsp;Dominik Linz","doi":"10.1016/j.cmpb.2025.108798","DOIUrl":"10.1016/j.cmpb.2025.108798","url":null,"abstract":"<div><h3>Background</h3><div>It is unclear, whether photoplethysmography (PPG) waveforms from wearable devices can differentiate between supraventricular and ventricular arrhythmias.</div><div>We assessed, whether a neural network-based classifier can distinguish the origin of PPG pulse waveforms.</div></div><div><h3>Methods</h3><div>In thirty patients undergoing invasive electrophysiological (EP) studies for narrow complex tachycardia, PPG waveforms were recorded using a PPG wristband (Empatica E4) in parallel to 12-lead surface electrocardiograms (ECGs) and intracardiac bipolar electrograms. PPG waveforms were annotated to either atrial (AP, supraventricular) or ventricular pacing (VP) based on bipolar electrograms, ECGs and stimulation protocols. 25 221 samples were split into training, testing, and validation data sets and used to develop, optimize and validate a residual network based on convolutional layers for classifying PPG waveforms according to their origin into AP or VP.</div></div><div><h3>Results</h3><div>Datasets were complete for 27 patients. 74 % were female, median age was 53 (range 18, 78) years and median BMI was 27±5 kg/m². The electrophysiological study revealed typical atrioventricular nodal re-entrant tachycardias in 63 %, atrial tachycardias in 15 % and no inducible tachyarrhythmias in 12 % of patients. On an independent patient level, correct prediction was possible in ∼73 % for AP and ∼59 % for VP. With adaptive performance built on previous patient-specific annotations, the classifier correctly predicted the origins of PPG-derived pulse waves in ∼97 % for AP and ∼95 % for VP.</div></div><div><h3>Conclusions</h3><div>A neural network trained on ground truth PPG data collected during EP studies could distinguish between supraventricular or ventricular origin from PPG waveforms alone.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108798"},"PeriodicalIF":4.9,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model","authors":"Divya Govindaraju,&nbsp;Sutha Subbian,&nbsp;S. Nambi Narayanan","doi":"10.1016/j.cmpb.2025.108799","DOIUrl":"10.1016/j.cmpb.2025.108799","url":null,"abstract":"<div><h3>Background</h3><div>Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cognitive decline, seizures, and Alzheimer’s disease. As neurons depend on glucose for energy, fluctuations in glucose levels can disrupt sodium (Na⁺) and potassium (K⁺) ion channel dynamics, leading to altered membrane potential. Modeling these ionic changes enables the simulation of neuronal responses under glycemic extremes, providing valuable insights for risk assessment and personalized treatment.</div></div><div><h3>Method</h3><div>The methodology utilizes Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) to classify hyperglycemic and hypoglycemic events based on variations in blood glucose levels. A glucose-sensing neuron model is developed using the Hodgkin-Huxley (HH) framework to examine how glycemic fluctuations influence Na⁺ and K⁺ channel conductance. The study uniquely alters maximal conductance values to precisely simulate the effects of hyper- and hypoglycemia on ion channel behaviour and neuronal excitability.</div></div><div><h3>Results</h3><div>The blood glucose classification results indicate that the CNN classifier effectively detects hyperglycemia and hypoglycemia, achieving an accuracy of 90.23 %, sensitivity of 87.45 %, specificity of 88.56 %, and precision of 89.31 %. Computational modeling shows that hyperglycemia decreases Na⁺ currents and increases K⁺ conductance, reducing neuronal excitability. In contrast, hypoglycemia increases Na⁺ activity and decreases K⁺ conductance, leading to excessive neuronal firing and rapid action potentials.</div></div><div><h3>Conclusion</h3><div>The proposed glucose-sensing neuron model captures how glycemic variations affect Na⁺ and K⁺ conductance and neuronal excitability. Integrating machine learning with HH modeling enables risk assessment of hypoglycemia-induced neuronal hyperexcitability and seizures, as well as hyperglycemia-associated insulin resistance and long-term risk of cognitive decline and Alzheimer’s disease.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108799"},"PeriodicalIF":4.9,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis","authors":"Asaf Raza ,&nbsp;Antonella Guzzo ,&nbsp;Michele Ianni ,&nbsp;Rosamaria Lappano ,&nbsp;Alfredo Zanolini ,&nbsp;Marcello Maggiolini ,&nbsp;Giancarlo Fortino","doi":"10.1016/j.cmpb.2025.108768","DOIUrl":"10.1016/j.cmpb.2025.108768","url":null,"abstract":"<div><div>Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federated Learning in Medical Imaging (FL-MI), published in reputable venues over the past five years. We adopt the PRISMA methodology, categorizing and analyzing the existing body of research in FL-MI. Our analysis identifies common trends, challenges, and emerging strategies for implementing FL in medical imaging, including handling data heterogeneity, privacy concerns, and model performance in non-IID settings. The paper also highlights the most widely used datasets and a comparison of adopted machine learning models. Moreover, we examine FL frameworks in FL-MI applications, such as tumor detection, organ segmentation, and disease classification. We identify several research gaps, including the need for more robust privacy protection. Our findings provide a comprehensive overview of the current state of FL-MI and offer valuable directions for future research and development in this rapidly evolving field.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108768"},"PeriodicalIF":4.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis","authors":"Rahat Shahrior ,&nbsp;Salwa Tamkin ,&nbsp;Mohammad Badhruddouza Khan ,&nbsp;Ahmed Jebail Meraj ,&nbsp;Hanif Bhuiyan","doi":"10.1016/j.cmpb.2025.108793","DOIUrl":"10.1016/j.cmpb.2025.108793","url":null,"abstract":"<div><div><em>Background and Objective:</em> In search of a key driver for the invasive growth of cancer metastasis, AKT2 is found to be exceptionally expressed in colorectal cancer and its metastasis. Again, exceeding genomic arrangements of AKT2 can be held responsible for HGSC (High-grade serous ovarian cancer) and breast cancer cell metastasis. FDA-approved capivasertib, a potential drug targeting the AKT signaling pathway, has a few side effects such as plausible alterations of liver function and gastrointestinal issues. Hence, this research aims to detect compounds with higher drug potency for selective AKT2 inhibition to encounter the incidence of different types of cancer cell metastasis. <em>Methods:</em> Eight machine-learning models were engaged to classify active and inactive drug candidates among 1148 collected compounds from the CHEMBL database. Potential drug candidates with greater <em>IC</em>₅₀ value and no Lipinski violations were then addressed to molecular docking and molecular dynamics simulation using PyRx, AutoDock Vina and Desmond package. <em>Results:</em> From docking studies, three of the initial drug candidates provided greater binding affinities within a range from -10.9 to -9.8 kcal/mol, comparable to that of Capivasertib and backed up by post-docking MM/GBSA analysis. Again, the prediction of pharmacokinetic properties and bioactivity scores of drug candidates revealed their drug-likeliness and safer ADMET profiles for future clinical trials. Finally, 100 ns MD simulation computation for these lead compounds exhibited greater stability and drug potency during interactions with AKT2 protein, followed by PCA and DCCM analysis. <em>Conclusion:</em> However, future in-vivo research can ascertain whether our proposed drug candidates can pass the standard clinical trials as publicly accessible novel drug targets.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108793"},"PeriodicalIF":4.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}