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Article Dans Une Revue Frontiers in Neuroscience Année : 2024

Multiple Sclerosis Clinical Forms Classification Study with Graph Convolutional Networks

Résumé

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsingremitting, secondary progressive or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e. T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.
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Dates et versions

hal-04373468 , version 1 (05-01-2024)

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Enyi Chen, Berardino Barile, Françoise Durand-Dubief, Thomas Grenier, Dominique Sappey-Marinier. Multiple Sclerosis Clinical Forms Classification Study with Graph Convolutional Networks. Frontiers in Neuroscience, inPress, ⟨10.3389/fnins.2023.1268860⟩. ⟨hal-04373468⟩
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