Predicting 5-Year EDSS in Multiple Sclerosis with LSTM Networks: A Deep Learning Approach to Disease Progression.
Journal:
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Published Date:
Apr 1, 2025
Abstract
BACKROUNDS: Multiple Sclerosis (MS) is a neurodegerative disease that is common worldwide, has no definitive cure yet, and negatively affects the individual's quality of life due to disease-related disability. Predicting disability in MS is difficult because of the complex nature of the disease. The primary goal of treating individuals with MS is to prevent or reduce irreversible neurological damage throughout the therapeutic course. Considering the importance of predicting disability MS in the early stage, in this study, we aimed to predict the 5th year score of the Extended Disability Status Scale (EDSS), which is used to measure disability levels in MS patients and allows for a comprehensive assessment of neurological functions. For this purpose, Long Short-Term Memory (LSTM), a special type of Recurrent Neural Network (RNN), designed specifically to analyze data and learn long-term relationships, was used in our study.
