Computers in biology and medicine最新文献

{"title":"Fast automated creation of digital twins for virtual mechanical testing of ovine fractured tibiae","authors":"Alireza Ariyanfar ,&nbsp;Mehran Bahrami ,&nbsp;Karina Klein ,&nbsp;Brigitte von Rechenberg ,&nbsp;Salim Darwiche ,&nbsp;Hannah L. Dailey","doi":"10.1016/j.compbiomed.2025.110268","DOIUrl":"10.1016/j.compbiomed.2025.110268","url":null,"abstract":"<div><div>Virtual mechanical testing with image-based digital twins enables subject-specific insights about the mechanical progression of bone fracture healing directly from imaging data. However, this technique is currently limited by the need for commercial software packages that require manual input to create finite element (FE) models from computed tomography (CT) scans. The purpose of this study was to develop automated image analysis algorithms that can create subject-specific models from CT scans without a human in the loop. Two competing techniques were developed and tested on an imaging dataset consisting of 26 intact and 44 osteotomized ovine tibiae. In both techniques, the raw image was cropped to an efficient bounding box, downsampled, segmented by an element-formation threshold, and cleaned up for efficient FE analysis using voxel-based meshes. The key difference between contour-free (CFT) and snake-reliant (SRT) techniques was threshold- and contour-based segmentation of images, respectively, before bounding box detection. The contours were detected using a snake that balanced desired aspects of the contours through energy minimization. Virtual torsion tests were performed and the results were validated by comparison to ground-truth experimental data. The CFT and SRT models produced nearly identical predictions of virtual torsional rigidity and both methods reliably replicated the physical tests. Models generated by SRT were faster to solve, but model preparation and solution combined was faster by CFT. Automatic digital twin creation by CFT is therefore recommended except where other downstream analyses require systematic spatial data sampling of the bone, which is only achieved by SRT.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110268"},"PeriodicalIF":7.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899809","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":"Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments","authors":"Vithurabiman Senthuran ,&nbsp;Uthayasanker Thayasivam ,&nbsp;Iynkaran Natgunanathan ,&nbsp;Keshav Sood ,&nbsp;Yong Xiang","doi":"10.1016/j.compbiomed.2025.110234","DOIUrl":"10.1016/j.compbiomed.2025.110234","url":null,"abstract":"<div><div>The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented Reality has led to the continuous collection and streaming of vast amounts of sensitive biometric data. Among the biometric signals collected, ECG (electrocardiogram) stands out given its critical role in healthcare, particularly for the diagnosis and management of cardiovascular diseases. Numerous studies have demonstrated that ECG contains traits to distinctively identify a person. As a result, the need for anonymization methods is becoming increasingly crucial to protect personal privacy while ensuring the integrity of health data for effective clinical utility. Although many anonymization methods have been proposed in the literature, there has been limited exploration into their ability to preserve data integrity while complying with stringent data protection regulations. More specifically, the utility of anonymized signal and the privacy level achieved often present a trade-off that has not been thoroughly addressed. This paper analyzes the trade-off between balancing privacy protection with the preservation of health data integrity in ECG signals focusing on memory-efficient anonymization techniques that are suitable for real-time or streaming applications and do not require heavy memory computation. Moreover, we introduce an analytical framework to evaluate the privacy preservation methods alongside health integrity, incorporating state-of-the-art disease and person identifiers. We also propose a novel metric that assists users in selecting an anonymization method based on their desired trade-off between health insights and privacy protection. The experimental results demonstrate the impact of the de-identification techniques on critical downstream tasks, such as Arrhythmia detection and Myocardial Infarction detection along with identification performance, while statistical analysis reveals the biometric nature of ECG signals. The findings highlight the limitations of using such anonymization methods and models, emphasizing the need for approaches that maintain the clinical relevance of ECG data in real-time and streaming applications, particularly in memory-constrained environments.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110234"},"PeriodicalIF":7.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891337","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":"Multimodal large language models as assistance for evaluation of thyroid-associated ophthalmopathy","authors":"Bo Ram Kim ,&nbsp;Joon Yul Choi ,&nbsp;Tae Keun Yoo","doi":"10.1016/j.compbiomed.2025.110301","DOIUrl":"10.1016/j.compbiomed.2025.110301","url":null,"abstract":"<div><div>This study evaluated the potential of multimodal AI chatbots, specifically ChatGPT-4o, in assessing thyroid-associated ophthalmopathy (TAO) through the Clinical Activity Score (CAS). Using publicly available case reports and datasets, ChatGPT-4o was tasked with generating a web-based CAS calculator and estimating CAS from external ocular photographs. Its predictions were compared with CAS evaluations by ophthalmologists and convolutional neural network (CNN) models, including ResNet50. Receiver operating characteristic (ROC) areas under the curve (AUCs) were calculated for the assessment of active TAO (CAS ≥3). ChatGPT-4o demonstrated high accuracy, with mean absolute errors of 0.39 and 0.45 compared to reference ophthalmologist scores across two datasets, outperforming both Gemini Advanced and ResNet50 in identifying active TAO. In the preoperative and pre-treatment datasets, ChatGPT-4o achieved ROC-AUCs of 0.974 and 0.990, respectively, significantly exceeding the performance of ResNet50 (0.770 and 0.623). Both ChatGPT-4o and Customized GPTs achieved identical results, suggesting robust performance without the need for further customization. The AI chatbot effectively processed both text- and image-based inputs, providing detailed explanations for its CAS estimates and creating a user-friendly calculator for rapid and accessible TAO evaluation. ChatGPT-4o thus can offer a reliable tool for TAO assessment, outperforming traditional CNN-based models. Its ability to generate a CAS calculator without prior training or coding expertise highlights its practical utility for clinical ophthalmology. This study's limitations included a small sample size, lack of real-world validation, reliance on photos without patient metadata, and challenges in repeatability. Future studies should aim to validate its effectiveness in real-world clinical settings.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110301"},"PeriodicalIF":7.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891338","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":"An explainable adaptive channel weighting-based deep convolutional neural network for classifying renal disorders in computed tomography images","authors":"G. Loganathan,&nbsp;M. Palanivelan","doi":"10.1016/j.compbiomed.2025.110220","DOIUrl":"10.1016/j.compbiomed.2025.110220","url":null,"abstract":"<div><div>Renal disorders are a significant public health concern and a cause of mortality related to renal failure. Manual diagnosis is subjective, labor-intensive, and depends on the expertise of nephrologists in renal anatomy. To improve workflow efficiency and enhance diagnosis accuracy, we propose an automated deep learning model, called EACWNet, which incorporates adaptive channel weighting-based deep convolutional neural network and explainable artificial intelligence. The proposed model categorizes renal computed tomography images into various classes, such as cyst, normal, tumor, and stone. The adaptive channel weighting module utilizes both global and local contextual insights to refine the final feature map channel weights through the integration of a scale-adaptive channel attention module in the higher convolutional blocks of the VGG-19 backbone model employed in the proposed method. The efficacy of the EACWNet model has been assessed using a publicly available renal CT images dataset, attaining an accuracy of 98.87% and demonstrating a 1.75% improvement over the backbone model. However, this model exhibits class-wise precision variation, achieving higher precision for cyst, normal, and tumor cases but lower precision for the stone class due to its inherent variability and heterogeneity. Furthermore, the model predictions have been subjected to additional analysis using the explainable artificial intelligence method such as local interpretable model-agnostic explanations, to visualize better and understand the model predictions.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110220"},"PeriodicalIF":7.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896128","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":"Deep learning meets marine biology: Optimized fused features and LIME-driven insights for automated plankton classification","authors":"Muhammad Hassan ,&nbsp;Giovanna Salbitani ,&nbsp;Simona Carfagna ,&nbsp;Javed Ali Khan","doi":"10.1016/j.compbiomed.2025.110273","DOIUrl":"10.1016/j.compbiomed.2025.110273","url":null,"abstract":"<div><div>Plankton are microorganisms that play an important role in marine food webs as primary producers in the trophic web. Traditional plankton identification methods using manual microscopy and sampling are time-consuming, labor-intensive, and prone to errors. Deep learning has improved the automation of plankton identification, but it remains challenging to achieve high accuracy and efficiency in computation with limited labeled data. In this paper, we proposed an improved plankton classification model that is more accurate and interpretable. We train two models, InceptionResNetV2 (transfer learning) and DeepPlanktonNet (from scratch), on the WHOI dataset. We utilize feature fusion to supplement feature representation, merging the outputs of both models. Feature selection is achieved through the Whale Optimization Algorithm (WOA), eliminating redundancy and making it more computationally efficient. Additionally, we also employ Local Interpretable Model-agnostic Explanations (LIME) to make the model more interpretable and gain insights into how the model makes decisions. Additionally, feature selection using WOA reduces feature space and has less inference and computational cost. Our method achieves a classification accuracy of 98.79 %, which is better than previous state-of-the-art methods. For robustness testing, we train nine machine learning classifiers on the optimized features. By significantly improving classification accuracy and speed, our method enables large-scale ecological surveys, water quality monitoring, and biodiversity studies. These advances allow researchers to and environmental scientists to automate plankton classification more reliably, supporting marine conservation and resource management.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110273"},"PeriodicalIF":7.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896129","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":"GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data","authors":"Hediyeh Talebi ,&nbsp;Shokoofeh Ghiam ,&nbsp;Asiyeh Mirzaei Koli ,&nbsp;Pourya Naderi Yeganeh ,&nbsp;Changiz Eslahchi","doi":"10.1016/j.compbiomed.2025.110283","DOIUrl":"10.1016/j.compbiomed.2025.110283","url":null,"abstract":"<div><h3>Objective</h3><div>To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.</div></div><div><h3>Methods</h3><div>Using scRNA-seq data from PBMCs of AD patients and cognitively normal controls, we developed a deep learning framework that integrates autoencoders, classifiers, and discriminators. This approach analyzed gene expression across various immune cell types—including T cells, B cells, NK cells, and monocytes—by combining both differentially expressed genes (DEGs) and subtle genetic variations typically overlooked by conventional methods. Enrichment analyses were then conducted using Gene Ontology (GO), KEGG pathways, and protein-protein interaction (PPI) networks to assess the biological relevance of the identified genes.</div></div><div><h3>Results</h3><div>Key genes, such as <em>ZFP36L2</em>, <em>PNRC1</em>, <em>DUSP1</em>, <em>BTG1</em>, <em>YBX1</em>, and <em>CYBA</em>, were identified as significant regulators of inflammation, apoptosis, and cell proliferation. Their overexpression in peripheral immune cells was linked to neuroinflammation, a critical factor in AD progression. Additionally, an observed overlap between aging-associated and AD-related genes reinforced the interconnected nature of these processes. The deep learning model achieved high precision, recall, and F1-scores across T cells, B cells, and NK cells, while Random Forest classifiers effectively managed constraints in monocyte data.</div></div><div><h3>Conclusion</h3><div>Combining scRNA-seq with deep learning provides a powerful non-invasive strategy for the early detection of AD by identifying novel blood-based biomarkers. This integrative approach not only enhances our understanding of immune regulation and neuroinflammatory pathways in AD but also paves the way for innovative diagnostic and therapeutic strategies.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110283"},"PeriodicalIF":7.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888276","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":"Cancer type and survival prediction based on transcriptomic feature map","authors":"Ming Yan ,&nbsp;Zirou Dong ,&nbsp;Zhaopo Zhu ,&nbsp;Chengliang Qiao ,&nbsp;Meizhi Wang ,&nbsp;Zhixia Teng ,&nbsp;Yongqiang Xing ,&nbsp;Guojun Liu ,&nbsp;Guoqing Liu ,&nbsp;Lu Cai ,&nbsp;Hu Meng","doi":"10.1016/j.compbiomed.2025.110267","DOIUrl":"10.1016/j.compbiomed.2025.110267","url":null,"abstract":"<div><div>This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA database, a pan-cancer transcriptomic feature map was constructed through data cleaning, feature extraction, and visualization. Using Inception network and gated convolutional modules yielded a pan-cancer classification accuracy of 91.8 %. Additionally, by extracting 31 differential genes from different cancer feature maps, an interaction network diagram was drawn, identifying two key genes, ANXA5 and ACTB. These genes are potential biomarkers related to cancer progression, angiogenesis, metastasis, and treatment resistance. Survival prediction analysis on 10 cancer types, combined with feature maps and data amplification, cancer survival prediction accuracy reached from 0.75 to 0.91. This transcriptomic feature map provides a novel approach for cancer omics analysis, to facilitate personalized treatments and reflecting individual differences.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110267"},"PeriodicalIF":7.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891410","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":"Enhancing clinical decision support with physiological waveforms — A multimodal benchmark in emergency care","authors":"Juan Miguel Lopez Alcaraz ,&nbsp;Hjalmar Bouma ,&nbsp;Nils Strodthoff","doi":"10.1016/j.compbiomed.2025.110196","DOIUrl":"10.1016/j.compbiomed.2025.110196","url":null,"abstract":"<div><h3>Background:</h3><div>AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including raw waveform signals, remains underexplored in clinical decision support.</div></div><div><h3>Methods:</h3><div>We present a dataset and benchmarking protocol designed to advance multimodal decision support in emergency care. Our models utilize demographics, biometrics, vital signs, laboratory values, and electrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses and patient deterioration.</div></div><div><h3>Results:</h3><div>The diagnostic model achieves area under the receiver operating curve (AUROC) scores above 0.8 for 609 out of 1,428 conditions, covering both cardiac (e.g., myocardial infarction) and non-cardiac (e.g., renal disease, diabetes) diagnoses. The deterioration model attains AUROC scores above 0.8 for 14 out of 15 targets, accurately predicting critical events such as cardiac arrest, mechanical ventilation, ICU admission, and mortality.</div></div><div><h3>Conclusions:</h3><div>Our study highlights the positive impact of incorporating raw waveform data into decision support models, improving predictive performance. By introducing a unique, publicly available dataset and baseline models, we provide a foundation for measurable progress in AI-driven decision support for emergency care.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110196"},"PeriodicalIF":7.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888082","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":"EB-YOLO:An efficient and lightweight blood cell detector based on the YOLO algorithm","authors":"Boyue Wu ,&nbsp;Shilun Feng ,&nbsp;Shuyue Jiang ,&nbsp;Shaobo Luo ,&nbsp;Xi Zhao ,&nbsp;Jianlong Zhao","doi":"10.1016/j.compbiomed.2025.110288","DOIUrl":"10.1016/j.compbiomed.2025.110288","url":null,"abstract":"<div><div>Blood cell detection is an important part of medical diagnosis. Object detection is trending for blood cell analysis, with research focusing on high-precision neural network models. However, these models have complex architectures and high computational costs. They cannot achieve rapid detection on low-end devices. Although lightweight models can greatly enhance the detection speed and achieve the real-time detection on low-end devices, their accuracy is poor in complex tasks. The development of efficient and highly accurate blood cell detectors for environments with limited computational resources is of great practical value. This study proposes an Efficient Blood Cell Detector based on YOLO (EB-YOLO) for blood cell detection. The model uses ShuffleNet as the backbone network for feature extraction to reduce the number of parameters and computational load. It incorporates the Convolutional Block Attention Module (CBAM) to enhance feature representation. In the neck network, Adaptive Spatial Feature Fusion (ASFF) is used for feature integration to improve multi-scale target feature extraction. Depth-wise separable convolution replaces standard convolution to reduce parameters while maintaining performance. Experimental results on the BCCD dataset show that the proposed model achieves 92.1 % mAP@50 %, the computational complexity is only 0.9 GFLOPs, and the number of parameters is 0.289M. The comparison results of the inference speed on Raspberry PI 5 show that the detection speed of the model is better than the classic YOLO algorithm model. The proposed method successfully balances lightweight design and high accuracy, which shows promise for deployment on low-end embedded systems.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110288"},"PeriodicalIF":7.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888053","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":"DeepValve: The first automatic detection pipeline for the mitral valve in Cardiac Magnetic Resonance imaging","authors":"Giulia Monopoli ,&nbsp;Daniel Haas ,&nbsp;Ashay Singh ,&nbsp;Eivind Westrum Aabel ,&nbsp;Margareth Ribe ,&nbsp;Anna Isotta Castrini ,&nbsp;Nina Eide Hasselberg ,&nbsp;Cecilie Bugge ,&nbsp;Christian Five ,&nbsp;Kristina Haugaa ,&nbsp;Nickolas Forsch ,&nbsp;Vajira Thambawita ,&nbsp;Gabriel Balaban ,&nbsp;Mary M. Maleckar","doi":"10.1016/j.compbiomed.2025.110211","DOIUrl":"10.1016/j.compbiomed.2025.110211","url":null,"abstract":"<div><div>Mitral valve (MV) assessment is key to diagnosing valvular disease and to addressing its serious downstream complications. Cardiac magnetic resonance (CMR) has become an essential diagnostic tool in MV disease, offering detailed views of the valve structure and function, and overcoming the limitations of other imaging modalities. Automated detection of the MV leaflets in CMR could enable rapid and precise assessments that enhance diagnostic accuracy. To address this gap, we introduce DeepValve, the first deep learning (DL) pipeline for MV detection using CMR. Within DeepValve, we tested three valve detection models: a keypoint-regression model (UNET-REG), a segmentation model (UNET-SEG) and a hybrid model based on keypoint detection (DSNT-REG). We also propose metrics for evaluating the quality of MV detection, including Procrustes-based metrics (UNET-REG, DSNT-REG) and customized Dice-based metrics (UNET-SEG). We developed and tested our models on a clinical dataset comprising 120 CMR images from patients with confirmed MV disease (mitral valve prolapse and mitral annular disjunction). Our results show that DSNT-REG delivered the best regression performance, accurately locating landmark locations. UNET-SEG achieved satisfactory Dice and customized Dice scores, also accurately predicting valve location and topology. Overall, our work represents a critical first step towards automated MV assessment using DL in CMR and paving the way for improved clinical assessment in MV disease.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110211"},"PeriodicalIF":7.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891411","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}