Abstract #0776
Classification of MS patients into disability levels using deep learning approaches based solely on routinely-acquired MRI
Llucia Coll1, Pere Carbonell-Mirabent1, Álvaro Cobo-Calvo1, Georgina Arrambide1, Ángela Vidal-Jordana1, Manuel Comabella1, Joaquín Castilló1, Breogán Rodríguez-Acevedo1, Ana Zabalza1, Ingrid Galán1, Luciana Midaglia1, Carlos Nos1, Annalaura Salerno2, Cristina Auger2, Manel Alberich2, Jordi Río1, Jaume Sastre-Garriga1, Arnau Oliver3, Xavier Montalban1, Àlex Rovira2, Mar Tintoré1, Deborah Pareto2, Xavier Lladó3, and Carmen Tur1
1Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain, 2Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain. Universitat Autònoma de Barcelona, Barcelona, Spain, 3Research institute of Computer Vision and Robotics, University of Girona, Girona, Spain
Synopsis
The ability of existing MRI biomarkers to predict MS patients’ prognosis is limited and inaccurate to be used at the individual level. We aimed to assess the ability of Convolutional Neural Networks (CNNs) to classify relapse-onset MS patients into non-disabled and markedly disabled using only MRI images. T1w and T2w-FLAIR images of 538 MS patients were used to train and test two CNN approaches which were compared also with (conventional) logistic regression models. The results showed that the CNN models performed better, having the intrinsic potential to improve after the inclusion of regional priors and other valuable clinical data.
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