Longitudinal Image

Longitudinal image analysis focuses on extracting meaningful information from sequences of images acquired from the same subject over time, aiming to track changes and predict future states. Current research emphasizes the development of deep learning models, including transformers, diffusion models, and neural ordinary differential equations (NODEs), to handle the complexities of high-dimensionality, irregular sampling, and data sparsity inherent in longitudinal datasets. These advancements are significantly impacting various fields, enabling more accurate disease progression monitoring (e.g., in MS, Alzheimer's, and eye diseases), improved medical image synthesis for data augmentation, and more efficient diagnostic tools through automated quality control and report generation. The ultimate goal is to enhance clinical decision-making and personalize patient care.