Artificial Intelligence in Medicine最新文献

{"title":"Deep learning for early detection of chronic kidney disease stages in diabetes patients: A TabNet approach","authors":"Md Nakib Hayat Chowdhury ,&nbsp;Mamun Bin Ibne Reaz ,&nbsp;Sawal Hamid Md Ali ,&nbsp;María Liz Crespo ,&nbsp;Shamim Ahmad ,&nbsp;Ghassan Maan Salim ,&nbsp;Fahmida Haque ,&nbsp;Luis Guillermo García Ordóñez ,&nbsp;Md. Johirul Islam ,&nbsp;Taher Muhammad Mahdee ,&nbsp;Kh Shahriya Zaman ,&nbsp;Md Shahriar Khan Hemel ,&nbsp;Mohammad Arif Sobhan Bhuiyan","doi":"10.1016/j.artmed.2025.103153","DOIUrl":"10.1016/j.artmed.2025.103153","url":null,"abstract":"<div><div>Chronic kidney disease (CKD) poses a significant risk for diabetes patients, often leading to severe complications. Early and accurate CKD stage detection is crucial for timely intervention. However, it remains challenging due to its asymptomatic progression, the oversight of routine CKD tests during diabetes checkups, and limited access to nephrologists. This study aimed to address these challenges by developing a multiclass CKD stage prediction model for diabetes patients using longitudinal data from the Chronic Renal Insufficiency Cohort (CRIC) study. A novel iterative backward feature selection strategy was employed to determine key predictors of the CKD stage. TabNet, an attention-based deep learning architecture, was used to build classification models in complete and simplified categories. The complete model used 31 features, including complex kidney biomarkers, while the simplified model used 15 features readily available from routine checkups. The performance of TabNet was compared against traditional tree-based ensemble methods (XGBoost, random forest, AdaBoost) and a multi-layer perceptron. Model-specific and model-agnostic explainable AI (XAI) techniques were applied to interpret model decisions, enhancing the transparency and clinical applicability of the proposed approach. The TabNet models demonstrated superior performance, achieving 94.06 % and 92.71 % accuracy in cross-validation for the complete and simplified models, respectively, and 91.00 % and 88.00 % accuracy on test sets. XAI analysis identified serum creatinine, cystatin C, sex, and age as the most influential factors in CKD stage classification. The proposed TabNet models offer a robust approach for early CKD severity detection in diabetes patients, potentially improving clinical decision-making and patient outcomes.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"166 ","pages":"Article 103153"},"PeriodicalIF":6.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929016","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":"Multi-modal signal integration for enhanced sleep stage classification: Leveraging EOG and 2-channel EEG data with advanced feature extraction","authors":"Mahdi Samaee ,&nbsp;Mehran Yazdi ,&nbsp;Daniel Massicotte","doi":"10.1016/j.artmed.2025.103152","DOIUrl":"10.1016/j.artmed.2025.103152","url":null,"abstract":"<div><div>This paper introduces an innovative approach to sleep stage classification, leveraging a multi-modal signal integration framework encompassing Electrooculography (EOG) and two-channel electroencephalography (EEG) data. We explore the utility of various feature extraction techniques, including Short-Time Fourier Transform (STFT), Wavelet Transform, and raw signal processing, alongside the utilization of neural networks as feature extractors. This unique combination allows us to harness the benefits of traditional feature extraction methods while capitalizing on the power of neural networks to enhance classification performance. Our comprehensive classifier evaluation encompasses a range of models, including Long Short-Term Memory (LSTM) networks and XGBoost. Remarkably, our results reveal exceptional performance with the XGBoost classifier, achieving an overall accuracy of 84.57 % and a macro-F1 score of 78.21 % on the Sleep-EDF expanded dataset, and an overall accuracy of 86.02 % and a macro-F1 score of 81.96 % on the ISRUC-Sleep dataset. Class-specific accuracies highlight its proficiency, particularly in detecting wake and N2 stages, solidifying its superiority among the classifiers tested. This amalgamation of feature sets, complemented by Principal Component Analysis (PCA) for dimensionality reduction, underscores its significance in yielding top-tier classification outcomes. The integration of traditional feature extraction methods with neural networks as feature extractors creates a robust and comprehensive system for sleep stage classification, offering the advantages of both approaches to enhance the accuracy and reliability of the results.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"166 ","pages":"Article 103152"},"PeriodicalIF":6.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906896","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":"CATI: A medical context-enhanced framework for diagnosis code assignment in the UK Biobank study","authors":"Yue Shen ,&nbsp;Jie Wang ,&nbsp;Zhe Wang ,&nbsp;Zhihao Shi ,&nbsp;Hanzhu Chen ,&nbsp;Zheng Wang ,&nbsp;Yukang Jiang ,&nbsp;Xiaopu Wang ,&nbsp;Chuandong Cheng ,&nbsp;Xueqin Wang ,&nbsp;Hongtu Zhu ,&nbsp;Jieping Ye","doi":"10.1016/j.artmed.2025.103136","DOIUrl":"10.1016/j.artmed.2025.103136","url":null,"abstract":"<div><div>Diagnosis codes are standard code format of diseases or medical conditions. This study is aimed at assigning diagnosis codes to patients in large-scale biobanks, particularly addressing the issue of missing codes for some patients. This is crucial for downstream disease-related tasks. While recent methods primarily rely on structured biobank data for code assignment, they often overlook the valuable medical context provided by textual information in the biobanks and hierarchical structure of the disease coding system. To address this gap, we have developed <strong>CATI</strong>, a medical context-enhanced framework for diagnosis <strong>C</strong>ode <strong>A</strong>ssignment by integrating <strong>T</strong>extual details derived from key features and disease h<strong>I</strong>erarchy. The study is based on the UK Biobank data and considers Phecodes and ICD-10 codes as standard disease formats. We start by representing ten informative codified features using their formal names and then integrate them into CATI as text embeddings, achieved through prompt tuning on the pre-trained language model BioBERT. Recognizing the hierarchical structure of diagnosis codes, we have developed a novel convolution layer in our method that effectively propagates logits between adjacent diagnosis codes. Evaluation results demonstrate that CATI outperforms existing state-of-the-art methods in terms of both Phecodes and ICD-10 codes, boasting at least a 5.16% improvement in average AUROC for unseen disease codes and an 8.68% rise in average AUPRC for disease codes with training instances ranging in (1000,10000]. This framework contributes to the formation of well-defined cohorts for downstream studies and offers a unique perspective for addressing complex healthcare tasks by incorporating vital medical context.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"166 ","pages":"Article 103136"},"PeriodicalIF":6.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921624","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 and clinical EEG data for multiple sclerosis: A systematic review","authors":"Badr Mouazen ,&nbsp;Ahmed Bendaouia ,&nbsp;El Hassan Abdelwahed ,&nbsp;Giovanni De Marco","doi":"10.1016/j.artmed.2025.103116","DOIUrl":"10.1016/j.artmed.2025.103116","url":null,"abstract":"<div><div>Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body’s immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques — including Deep Learning (DL) models — offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and DL models to EEG data for MS. It explores the methodologies used, with a focus on DL architectures such as Convolutional Neural Networks (CNNs) and hybrid models, and highlights recent advancements in ML techniques and EEG technologies that have significantly improved MS diagnosis and monitoring. The review addresses the challenges and potential biases in using ML-based EEG analysis for MS. Strategies to mitigate these challenges, including advanced preprocessing techniques, diverse training datasets, cross-validation methods, and explainable Artificial Intelligence (AI), are discussed. Finally, the paper outlines potential future applications and trends in ML for MS management. This review underscores the transformative potential of ML-enhanced EEG analysis in improving MS management, providing insights into future research directions to overcome existing limitations and further improve clinical practice.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"166 ","pages":"Article 103116"},"PeriodicalIF":6.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906895","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":"Developing novel dynamic prediction methods for survival time to analyze short-term and long-term progression of Alzheimer's disease","authors":"Chengfeng Zhang ,&nbsp;Shuyu Chen ,&nbsp;Yanjie Wang ,&nbsp;Pansheng Xue ,&nbsp;Yu Song ,&nbsp;Jiaqiao Ren ,&nbsp;Derun Zhou ,&nbsp;Zheng Chen","doi":"10.1016/j.artmed.2025.103140","DOIUrl":"10.1016/j.artmed.2025.103140","url":null,"abstract":"<div><div>Tracking and monitoring mild cognitive impairment (MCI) patients to intervene promptly at the imminent onset of Alzheimer's disease (AD) are crucial. However, existing dynamic survival prediction models for the conversion from MCI to AD are mostly based on hazard rates, which are less intuitive to interpret and require adherence to the proportional hazards assumption. To address this, we propose a Bayesian joint model (JM) based on the time scale indicator of the restricted mean survival time (RMST), which can capture the trajectories of multiple longitudinal covariates and dynamically predict the patient time to event. Using Monte Carlo simulation, it can be demonstrated that the JM method has a better prediction performance compared with the static model. To predict the dynamic progression of AD in MCI patients at different stages, based on the landmark (LM) method and the JM method for RMST, we developed an LM-based model for short-term dynamic prediction (LM-ST model) and a JM-based model for long-term dynamic prediction (JM-LT model) utilizing the ADNI database. The internal and external validation results indicate that the predictive performance of the LM-ST and JM-LT models surpasses that of the static RMST model. Additionally, an online web tool for the two dynamic prediction models was created for clinical application. In summary, we propose a novel method and combined it with the existing LM method for AD progression, which improves the predictive power and provides a scientific basis for medical decision-making.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"165 ","pages":"Article 103140"},"PeriodicalIF":6.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887876","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":"Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification","authors":"Aya Hage Chehade ,&nbsp;Nassib Abdallah ,&nbsp;Jean-Marie Marion ,&nbsp;Mathieu Hatt ,&nbsp;Mohamad Oueidat ,&nbsp;Pierre Chauvet","doi":"10.1016/j.artmed.2025.103135","DOIUrl":"10.1016/j.artmed.2025.103135","url":null,"abstract":"<div><div>U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our study, we tackled the challenge of precise segmentation and mask generation by developing a novel approach, using CycleGAN, that encompasses the areas affected by pathologies within the region of interest, allowing the extraction of relevant radiomic features linked to pathologies. Furthermore, we adopted a feature selection approach to focus the analysis on the most significant features. The results of our proposed pipeline are promising, with an average accuracy of 92.05% and an average AUC of 89.48% for the multi-label classification of effusion and infiltration acquired from the ChestX-ray14 dataset, using the XGBoost model. Furthermore, applying our methodology to the classification of the 14 diseases in the ChestX-ray14 dataset resulted in an average AUC of 83.12%, outperforming previous studies. This research highlights the importance of effective pathological mask generation and features selection for accurate classification of chest diseases. The promising results of our approach underscore its potential for broader applications in the classification of chest diseases.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"165 ","pages":"Article 103135"},"PeriodicalIF":6.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878612","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 informed machine learning based environmental risk score for hypertension in European adults","authors":"Jean-Baptiste Guimbaud ,&nbsp;Emilie Calabre ,&nbsp;Rafael de Cid ,&nbsp;Camille Lassale ,&nbsp;Manolis Kogevinas ,&nbsp;Léa Maître ,&nbsp;Rémy Cazabet","doi":"10.1016/j.artmed.2025.103139","DOIUrl":"10.1016/j.artmed.2025.103139","url":null,"abstract":"<div><h3>Background</h3><div>The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle with challenges including multicollinearity, non-linearity and confounding. To address these limitations, we introduce SEANN (Summary Effect Adjusted Neural Network) a novel approach that integrates pooled effect sizes—a form of domain knowledge—with neural networks to improve the analysis and interpretation of hypertension risk factors.</div></div><div><h3>Methods</h3><div>Based on data from 18,337 adults aged 40-65y participants in the GCAT cohort in Catalonia, covering a diverse selection of 53 environmental factors, we computed two environmental risk scores for hypertension prevalence using deep neural networks. An informed risk score using SEANN, integrating 11 different pooled effect size estimates from meta-analyses, and an agnostic counterpart for comparison. For each score, we computed Shapley values to extract and compare the learnt exposure-outcome relationships from each neural network model.</div></div><div><h3>Results</h3><div>The obtained predictive performances were similarly good for the agnostic NN and SEANN (AUC 0.7). However, we demonstrate substantial improvements in the scientific validity of the informed risk score captured relationships. Directly informed variables were closer to their corresponding relationships observed in literature and other non-informed variables were successfully adjusted with their direction of associations more in line with previous studies. The mean delta SHAP distance averaged over all variables of the relationships extracted with both models and those observed in the literature, was 6 times lower with SEANN compared with the agnostic NN. The most influential environmental variables within the informed risk score included smoking intensity, Mediterranean diet adherence, coffee consumption and sedentary behaviour.</div></div><div><h3>Conclusions</h3><div>This study demonstrates the added value of SEANN over conventional, purely data-driven machine learning approaches. By aligning learned relationships with established literature-based effect sizes, SEANN improves the disentanglement of exposure effects on hypertension.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"165 ","pages":"Article 103139"},"PeriodicalIF":6.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891298","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":"Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data","authors":"Shivani Gupta ,&nbsp;Saurabh Sharma ,&nbsp;Rajesh Sharma ,&nbsp;Joydeep Chandra","doi":"10.1016/j.artmed.2025.103134","DOIUrl":"10.1016/j.artmed.2025.103134","url":null,"abstract":"<div><div>In healthcare, predictive analysis using unstructured medical data is crucial for gaining insights into patient conditions and outcomes. However, unstructured data, which contains valuable patient information such as symptoms and medical histories, often presents challenges, including lengthy text sequences and incomplete data. To address these issues, we introduce a new framework named Hierarchical Attention-based Integrated Learning (HAIL), designed to predict in-hospital mortality and the duration of stay in the intensive care unit. HAIL combines hierarchical attention mechanisms with graph neural networks to effectively manage missing data and enhance outcome predictions. Our model iteratively refines embeddings, resulting in a more thorough analysis of electronic health record data. Experimental findings demonstrate a notable performance improvement of 2%–3% across various metrics when compared to existing benchmarks on standard datasets, highlighting HAIL’s effectiveness in time-sensitive clinical decision-making. Additionally, our analysis underscores the significance of patient networks in maintaining the robustness and consistent performance of the HAIL framework.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"165 ","pages":"Article 103134"},"PeriodicalIF":6.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873992","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":"Cascade learning in multi-task encoder–decoder networks for concurrent bone segmentation and glenohumeral joint clinical assessment in shoulder CT scans","authors":"Luca Marsilio ,&nbsp;Davide Marzorati ,&nbsp;Matteo Rossi ,&nbsp;Andrea Moglia ,&nbsp;Luca Mainardi ,&nbsp;Alfonso Manzotti ,&nbsp;Pietro Cerveri","doi":"10.1016/j.artmed.2025.103131","DOIUrl":"10.1016/j.artmed.2025.103131","url":null,"abstract":"<div><div>Osteoarthritis is a degenerative condition that affects bones and cartilage, often leading to structural changes, including osteophyte formation, bone density loss, and the narrowing of joint spaces. Over time, this process may disrupt the glenohumeral (GH) joint functionality, requiring a targeted treatment. Various options are available to restore joint functions, ranging from conservative management to surgical interventions, depending on the severity of the condition. This work introduces an innovative deep learning framework to process shoulder CT scans. It features the semantic segmentation of the proximal humerus and scapula, the 3D reconstruction of bone surfaces, the identification of the GH joint region, and the staging of three common osteoarthritic-related conditions: osteophyte formation (OS), GH space reduction (JS), and humeroscapular alignment (HSA). Each condition was stratified into multiple severity stages, offering a comprehensive analysis of shoulder bone structure pathology. The pipeline comprised two cascaded CNN architectures: 3D CEL-UNet for segmentation and 3D Arthro-Net for threefold classification. A retrospective dataset of 571 CT scans featuring patients with various degrees of GH osteoarthritic-related pathologies was used to train, validate, and test the pipeline. Root mean squared error and Hausdorff distance median values for 3D reconstruction were 0.22 mm and 1.48 mm for the humerus and 0.24 mm and 1.48 mm for the scapula, outperforming state-of-the-art architectures and making it potentially suitable for a PSI-based shoulder arthroplasty preoperative plan context. The classification accuracy for OS, JS, and HSA consistently reached around 90% across all three categories. The computational time for the entire inference pipeline was less than 15 s, showcasing the framework’s efficiency and compatibility with orthopedic radiology practice. The achieved reconstruction and classification accuracy, combined with the rapid processing time, represent a promising advancement towards the medical translation of artificial intelligence tools. This progress aims to streamline the preoperative planning pipeline, delivering high-quality bone surfaces and supporting surgeons in selecting the most suitable surgical approach according to the unique patient joint conditions.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"165 ","pages":"Article 103131"},"PeriodicalIF":6.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863792","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":"Human visual perception-inspired medical image segmentation network with multi-feature compression","authors":"Guangju Li ,&nbsp;Qinghua Huang ,&nbsp;Wei Wang ,&nbsp;Longzhong Liu","doi":"10.1016/j.artmed.2025.103133","DOIUrl":"10.1016/j.artmed.2025.103133","url":null,"abstract":"<div><div>Medical image segmentation is crucial for computer-aided diagnosis and treatment planning, directly influencing clinical decision-making. To enhance segmentation accuracy, existing methods typically fuse local, global, and various other features. However, these methods often ignore the negative impact of noise on the results during the feature fusion process. In contrast, certain regions of the human visual system, such as the inferotemporal cortex and parietal cortex, effectively suppress irrelevant noise while integrating multiple features—a capability lacking in current methods. To address this gap, we propose MS-Net, a medical image segmentation network inspired by human visual perception. MS-Net incorporates a multi-feature compression (MFC) module that mimics the human visual system’s processing of complex images, first learning various feature types and subsequently filtering out irrelevant ones. Additionally, MS-Net features a segmentation refinement (SR) module that emulates how physicians segment lesions. This module initially performs coarse segmentation to capture the lesion’s approximate location and shape, followed by a refinement step to achieve precise boundary delineation. Experimental results demonstrate that MS-Net not only attains state-of-the-art segmentation performance across three public datasets but also significantly reduces the number of parameters compared to existing models. Code is available at <span><span>https://github.com/guangguangLi/MS-Net</span><svg><path></path></svg></span></div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"165 ","pages":"Article 103133"},"PeriodicalIF":6.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869131","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}