1. Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI
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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, and Carmen Tur
- Subjects
Multiple sclerosis ,Structural MRI ,Deep learning ,Attention maps ,Disability ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Neurology. Diseases of the nervous system ,RC346-429 - 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
- Published
- 2023
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