  Alzheimer's Disease (AD) is a neurodegenerative condition characterized by
diverse progression rates among individuals, with changes in cortical thickness
(CTh) closely linked to its progression. Accurately forecasting CTh
trajectories can significantly enhance early diagnosis and intervention
strategies, providing timely care. However, the longitudinal data essential for
these studies often suffer from temporal sparsity and incompleteness,
presenting substantial challenges in modeling the disease's progression
accurately. Existing methods are limited, focusing primarily on datasets
without missing entries or requiring predefined assumptions about CTh
progression. To overcome these obstacles, we propose a conditional score-based
diffusion model specifically designed to generate CTh trajectories with the
given baseline information, such as age, sex, and initial diagnosis. Our
conditional diffusion model utilizes all available data during the training
phase to make predictions based solely on baseline information during inference
without needing prior history about CTh progression. The prediction accuracy of
the proposed CTh prediction pipeline using a conditional score-based model was
compared for sub-groups consisting of cognitively normal, mild cognitive
impairment, and AD subjects. The Bland-Altman analysis shows our
diffusion-based prediction model has a near-zero bias with narrow 95%
confidential interval compared to the ground-truth CTh in 6-36 months. In
addition, our conditional diffusion model has a stochastic generative nature,
therefore, we demonstrated an uncertainty analysis of patient-specific CTh
prediction through multiple realizations.
