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Robust forecasting of the future anatomical changes inflicted by an ongoingdisease is an extremely challenging task that is out of grasp even forexperienced healthcare professionals. Such a capability, however, is of greatimportance since it can improve patient management by providing information onthe speed of disease progression already at the admission stage, or it canenrich the clinical trials with fast progressors and avoid the need for controlarms by the means of digital twins. In this work, we develop a deep learningmethod that models the evolution of age-related disease by processing a singlemedical scan and providing a segmentation of the target anatomy at a requestedfuture point in time. Our method represents a time-invariant physical processand solves a large-scale problem of modeling temporal pixel-level changesutilizing NeuralODEs. In addition, we demonstrate the approaches to incorporatethe prior domain-specific constraints into our method and define temporal Diceloss for learning temporal objectives. To evaluate the applicability of ourapproach across different age-related diseases and imaging modalities, wedeveloped and tested the proposed method on the datasets with 967 retinal OCTvolumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposedmethod outperformed the related baseline models in the atrophy growthprediction. For Alzheimer's Disease, the proposed method demonstratedremarkable performance in predicting the brain ventricle changes induced by thedisease, achieving the state-of-the-art result on TADPOLE challenge.

Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data