Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators

• Published in: Academic radiology Vol. 31; no. 7; p. 2910
• Main Authors: Yan, Zichun, Shi, Zhuowei, Zhu, Qiyuan, Feng, Jinzhou, Liu, Yaou, Li, Yuxin, Zhou, Fuqing, Zhuo, Zhizheng, Ding, Shuang, Wang, Xiaohua, Yin, Feiyue, Tang, Yang, Lin, Bing, Li, Yongmei
• Format: Journal Article
• Language: English
• Published: United States 01.07.2024
• Subjects: Adult; Atrophy; Disability Evaluation; Disease Progression; Female; Gray Matter - diagnostic imaging; Gray Matter - pathology; Humans; Machine Learning; Magnetic Resonance Imaging - methods; Male; Middle Aged; Multiple Sclerosis, Relapsing-Remitting - diagnostic imaging; Multiple Sclerosis, Relapsing-Remitting - pathology; Retrospective Studies; Multiple sclerosis; Gray matter; Disease progression; Machine learning

To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine learning model. 145 and 50 RRMS patients with structural MRI and at least 1-ye...