  Personalized medicine based on medical images, including predicting future
individualized clinical disease progression and treatment response, would have
an enormous impact on healthcare and drug development, particularly for
diseases (e.g. multiple sclerosis (MS)) with long term, complex, heterogeneous
evolutions and no cure. In this work, we present the first stochastic causal
temporal framework to model the continuous temporal evolution of disease
progression via Neural Stochastic Differential Equations (NSDE). The proposed
causal inference model takes as input the patient's high dimensional images
(MRI) and tabular data, and predicts both factual and counterfactual
progression trajectories on different treatments in latent space. The NSDE
permits the estimation of high-confidence personalized trajectories and
treatment effects. Extensive experiments were performed on a large,
multi-centre, proprietary dataset of patient 3D MRI and clinical data acquired
during several randomized clinical trials for MS treatments. Our results
present the first successful uncertainty-based causal Deep Learning (DL) model
to: (a) accurately predict future patient MS disability evolution (e.g. EDSS)
and treatment effects leveraging baseline MRI, and (b) permit the discovery of
subgroups of patients for which the model has high confidence in their response
to treatment even in clinical trials which did not reach their clinical
endpoints.
