Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction
<|reference_start|>Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction: 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.<|reference_end|>
@article{xiao2024conditional,
    title={Conditional Score-Based Diffusion Model for Cortical Thickness
  Trajectory Prediction},
    author={Qing Xiao, Siyeop Yoon, Hui Ren, Matthew Tivnan, Lichao Sun, Quanzheng
  Li, Tianming Liu, Yu Zhang, and Xiang Li},
    journal={arXiv preprint arXiv:2403.06940},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06940},
    primaryClass={eess.IV cs.LG q-bio.QM}
}