학술논문
Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI
Document Type
article
Author
Llucia Coll; Deborah Pareto; Pere Carbonell-Mirabent; Álvaro Cobo-Calvo; Georgina Arrambide; Ángela Vidal-Jordana; Manuel Comabella; Joaquín Castilló; Breogán Rodríguez-Acevedo; Ana Zabalza; Ingrid Galán; Luciana Midaglia; Carlos Nos; Annalaura Salerno; Cristina Auger; Manel Alberich; Jordi Río; Jaume Sastre-Garriga; Arnau Oliver; Xavier Montalban; Àlex Rovira; Mar Tintoré; Xavier Lladó; Carmen Tur
Source
NeuroImage: Clinical, Vol 38, Iss , Pp 103376- (2023)
Subject
Language
English
ISSN
2213-1582
90301447
90301447
Abstract
The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of extracting image features not detectable through conventional methods. Additionally, the study of CNN-derived attention maps, which indicate the most relevant anatomical features for CNN-based decisions, has the potential to uncover key disease mechanisms leading to disability accumulation.From a cohort of patients prospectively followed up after a first demyelinating attack, we selected those with T1-weighted and T2-FLAIR brain MRI sequences available for image analysis and a clinical assessment performed within the following six months (N = 319). Patients were divided into two groups according to expanded disability status scale (EDSS) score: ≥3.0 and