Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data

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Adrian Tousignant, Paul Lemaître, Doina Precup, Douglas L. Arnold, Tal Arbel ;
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:483-492, 2019.

Abstract

We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net {{Szegedy et al.}} ({2015}) and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66 ± 0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701 ± 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.

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