A Deep-Learning Approach to Predicting Disease Progression in Multiple Sclerosis Using Magnetic Resonance Imaging
Loredana Storelli1, Matteo Azzimonti1,2, Mor Gueye1,2, Paolo Preziosa1,2, Carmen Vizzino1, Gioacchino Tedeschi3, Nicola De Stefano4, Patrizia Pantano5,6, Massimo Filippi1,2,7,8,9, and Maria A. Rocca1,2,9
1Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy, 2Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy, 3Department of Advanced Medical and Surgical Sciences, and 3T MRI-Center, University of Campania “Luigi Vanvitelli”, Maples, Italy, 4Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy, 5Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy, 6IRCCS NEUROMED, Pozzilli, Italy, 7Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy, 8Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy, 9Vita-Salute San Raffaele University, Milan, Italy
Artificial intelligence (AI) approaches have been applied in several fields of multiple sclerosis (MS) in recent years. However, their application to predict disease progression remains largely unexplored. In this study, we obtained a robust and accurate AI tool for predicting clinical and cognitive evolution at two years for MS patients, based on just T1-weighted and T2-weighted brain MRI scans at baseline visit, which exceeded the performance of two expert physicians blinded to patients’ clinical history. This algorithm has the potential to be an important tool to support physicians for a prompt recognition of MS patients at risk of disease worsening.
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