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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