{"title":"Prediction of Expanded Disability Status Scale in patients with MS using deep learning","authors":"","doi":"10.1016/j.compbiomed.2024.109143","DOIUrl":null,"url":null,"abstract":"<div><p>Multiple sclerosis (MS) is a chronic neurological condition that leads to significant disability in patients. Accurate prediction of disease progression, specifically the Expanded Disability Status Scale (EDSS), is crucial for personalizing treatment and improving patient outcomes. This study aims to develop a robust deep neural network framework to predict EDSS in MS patients using MRI scans. Our model demonstrates high accuracy and reliability in both lesion segmentation and disability classification tasks. For segmentation, the model achieves a Dice Coefficient of 0.87, a Jaccard Index of 0.79, sensitivity of 0.85, and specificity of 0.88. In classification, it attains an overall accuracy of 91.2 %, with a precision of 0.89, recall of 0.88, and an F1-Score of 0.885. Ablation studies highlight the significant impact of integrating T2-weighted and FLAIR images, improving accuracy from 85.7 % (T1-weighted alone) to 93.4 %. Comparative analysis with state-of-the-art methods demonstrates our model's superiority, outperforming Method A and Method B in both accuracy and F1-Score. Despite these advancements, challenges such as data quality, sample size, and computational complexity remain. Future research should focus on standardizing imaging protocols, incorporating larger and more diverse datasets, and optimizing model efficiency. Advancing deep learning architectures and utilizing multimodal data can enhance predictive power and facilitate real-time clinical applications. Our study significantly contributes to refining MS treatment strategies by providing a comprehensive evaluation of our model's performance and addressing key limitations. Accurate disability predictions enable personalized treatments, early interventions, and improved patient outcomes, thus enhancing the quality of life for individuals affected by MS.</p></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010482524012289","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Multiple sclerosis (MS) is a chronic neurological condition that leads to significant disability in patients. Accurate prediction of disease progression, specifically the Expanded Disability Status Scale (EDSS), is crucial for personalizing treatment and improving patient outcomes. This study aims to develop a robust deep neural network framework to predict EDSS in MS patients using MRI scans. Our model demonstrates high accuracy and reliability in both lesion segmentation and disability classification tasks. For segmentation, the model achieves a Dice Coefficient of 0.87, a Jaccard Index of 0.79, sensitivity of 0.85, and specificity of 0.88. In classification, it attains an overall accuracy of 91.2 %, with a precision of 0.89, recall of 0.88, and an F1-Score of 0.885. Ablation studies highlight the significant impact of integrating T2-weighted and FLAIR images, improving accuracy from 85.7 % (T1-weighted alone) to 93.4 %. Comparative analysis with state-of-the-art methods demonstrates our model's superiority, outperforming Method A and Method B in both accuracy and F1-Score. Despite these advancements, challenges such as data quality, sample size, and computational complexity remain. Future research should focus on standardizing imaging protocols, incorporating larger and more diverse datasets, and optimizing model efficiency. Advancing deep learning architectures and utilizing multimodal data can enhance predictive power and facilitate real-time clinical applications. Our study significantly contributes to refining MS treatment strategies by providing a comprehensive evaluation of our model's performance and addressing key limitations. Accurate disability predictions enable personalized treatments, early interventions, and improved patient outcomes, thus enhancing the quality of life for individuals affected by MS.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.