Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data
This work addresses the need for improved patient management and clinical trial enrichment in healthcare by predicting disease progression from medical imaging, though it appears incremental as it builds on existing NeuralODE methods with domain-specific constraints.
The authors tackled the problem of forecasting future anatomical changes in age-related diseases from a single medical scan, developing a deep learning method that models disease progression using NeuralODEs and achieved state-of-the-art results on the TADPOLE challenge for Alzheimer's Disease and outperformed baselines for Geographic Atrophy.
Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare professionals. Such a capability, however, is of great importance since it can improve patient management by providing information on the speed of disease progression already at the admission stage, or it can enrich the clinical trials with fast progressors and avoid the need for control arms by the means of digital twins. In this work, we develop a deep learning method that models the evolution of age-related disease by processing a single medical scan and providing a segmentation of the target anatomy at a requested future point in time. Our method represents a time-invariant physical process and solves a large-scale problem of modeling temporal pixel-level changes utilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate the prior domain-specific constraints into our method and define temporal Dice loss for learning temporal objectives. To evaluate the applicability of our approach across different age-related diseases and imaging modalities, we developed and tested the proposed method on the datasets with 967 retinal OCT volumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of 633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed method outperformed the related baseline models in the atrophy growth prediction. For Alzheimer's Disease, the proposed method demonstrated remarkable performance in predicting the brain ventricle changes induced by the disease, achieving the state-of-the-art result on TADPOLE challenge.