Szekely-Kohn Adam C, Castellani Marco, Espino Daniel M, Baronti Luca, Ahmed Zubair, Manifold William G K, Douglas Michael
School of Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
R Soc Open Sci. 2025 Jan 22;12(1):241052. doi: 10.1098/rsos.241052. eCollection 2025 Jan.
Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS. Published MS MRI-based computational studies can be divided into five categories: automated diagnosis; differentiation between lesion types and/or MS stages; differential diagnosis; monitoring and predicting disease progression; and synthetic MRI dataset generation. Collectively, these approaches show promise in assisting with MS diagnosis, monitoring of disease activity and prediction of future progression, all potentially contributing to disease management. Analysis quality using ML is highly dependent on the dataset size and variability used for training. Wider public access would mean larger datasets for experimentation, resulting in higher-quality analysis, permitting for more conclusive research. This narrative review provides an outline of the fundamentals of MS pathology and pathogenesis, diagnostic techniques and data types in computational analysis, as well as collating literature pertaining to the application of computational techniques to MRI towards developing a better understanding of MS.
多发性硬化症（MS）是一种发生于脑和脊髓的自身免疫性疾病，具有炎症和神经退行性特征。尽管成像技术的进步，尤其是磁共振成像（MRI），改善了诊断过程，但其病因不明，治愈方法仍然难以捉摸，且缺乏指导治疗的证据基础。机器学习（ML）等计算技术已开始用于了解MS。已发表的基于MS MRI的计算研究可分为五类：自动诊断；病变类型和/或MS阶段的区分；鉴别诊断；监测和预测疾病进展；以及合成MRI数据集生成。总体而言，这些方法在协助MS诊断、监测疾病活动和预测未来进展方面显示出前景，所有这些都可能有助于疾病管理。使用ML的分析质量高度依赖于用于训练的数据集大小和变异性。更广泛的公众访问意味着有更大的数据集用于实验，从而产生更高质量的分析，允许进行更具决定性的研究。这篇叙述性综述概述了MS病理学和发病机制的基本原理、计算分析中的诊断技术和数据类型，以及整理与将计算技术应用于MRI以更好地理解MS相关的文献。
