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Communication Dans Un Congrès Année : 2020

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

Résumé

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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Dates et versions

hal-03356638 , version 1 (28-09-2021)

Identifiants

  • HAL Id : hal-03356638 , version 1

Citer

Arnaud Attyé, Stenzel Cackowski, Alan Tucholka, Pauline Roca, Pascal Rubini, et al.. Breaking the clinico-radiological paradox in multiple sclerosis using machine learning. 28th Annual Meeting ISMRM, 2020, Aug 2020, Virtual Conference, France. ⟨hal-03356638⟩
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