Machine learning (ML) based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. In MS, ML techniques have been developed with two main objectives: 1) detection and segmentation of brain lesions and 2) classification of clinical forms and prediction of patient disability based on conventional or/and advanced MRI methods.
In this presentation, we review the recent developments in ML approaches including deep learning (DL) techniques based on conventional and/or advanced MRI biomarkers for clinical classification and monitoring as well as disability prediction.
Classical ML techniques such as Support Vector Machine (SVM), Random-Forest (RF), k-nearest neighbor (kNN), Linear Discriminant Analysis (LDA), initially used for lesion detection and segmentation, were compared with recent DL techniques. These methods include different types of neural networks such as Convolutional Neural Networks (CNN) and graph-based neural networks (GNN) that are designed for image and graph MRI data, respectively. In one hand, conventional MRI provides multispectral images such as pre- and post-gadolinium-contrast T1-weighted (T1w), T2-weighted (T2w), proton-density-weighted (PDw) or fluid-attenuated inversion recovery (FLAIR) for lesion detection and segmentation. On the other hand, advanced MRI techniques such tensor diffusion imaging (DTI) combined with graph models provide brain structural connectivity data allowing the extraction of global and local metrics for white matter (WM) characterization.
Recent developments of CNN based on conventional MRI images out performed ML techniques and provide excellent performances for lesion detection as well as for prediction of patient disability. Best results were obtained by measuring new and enlarging lesions (volume). Furthermore, GNN based on structural connectivity data out performed previous results for disability prediction. Finally, CNN and others ML methods are developed to improve the performances of classification or prediction, and further, to better visualize and interpret the CNN decision.
Deep learning frameworks based on 3D CNN or other advanced ML techniques provide to-day excellent performances for lesion segmentation and disability prediction on conventional MRI, and even better results, on WM fiber bundles and structural connectivity data. New CNN are improved by taking in account several parameters such data characteristics, measures uncertainty and by providing information on decision.