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Abstract #0041

Breaking the clinico-radiological paradox in multiple sclerosis using machine learning

Arnaud Attyé1,2, Stenzel Cackowski3, Alan Tucholka4, Pauline Roca4, Pascal Rubini4, Sebastien Verclytte5, Lucie Colas5, Juliette Ding5, Jean-François Budzik5, Felix Renard6, Emmanuel L Barbier3, Romain Casey7,8,9,10, Sandra Vukusic7,8, and François Cotton7,11
1Grenoble alpes university, Grenoble, France, 2Sydney Imaging Lab, Sydney university, Sydney, Australia, 3Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France, 4Pixyl Medical, Grenoble, France, 5Lille Catholic University, Lille, France, 6Laboratoire d'informatique de Grenoble, Grenoble, France, 7Claude Bernard Lyon 1 University, Lyon, France, 8Lyon University Hospital, Lyon, France, 9Observatoire Français de la Sclérose en Plaques, INSERM 1028 et CNRS UMR 5292, Lyon, France, 10EUGENE DEVIC EDMUS Foundation against multiple sclerosis, Lyon, France, 11CREATIS, CNRS UMR 5220 - INSERM U1206, Lyon, France

MRI is central to the study of white matter lesions in multiple sclerosis (MS). To date, the distribution of MS lesions, as evaluated on FLAIR imaging, has not been linked to patients’ disability prediction. Based on an international data challenge with 1500 MS patients and ground truth 2-year Expanded Disability Status Scale (EDSS), we have proposed an adaptive machine learning framework to predict the clinical disability. Here, we report the encouraging finding that our algorithm predicts the 2-year EDSS score with an accuracy estimated to 81%, only based on a single initial FLAIR sequence, added to sex and gender information.

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