Problem
Research questions and friction points this paper is trying to address.
Alzheimer's disease
continuous assessment
disease severity
neuroimaging
Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum
🤖 AI Summary
该研究通过提出一种基于纵向贝叶斯学习框架的疾病连续体定位方法(DCP)，从连续角度估计阿尔茨海默病严重程度，解决了现有AI方法仅限于离散诊断的问题。
📝 Abstract
Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosis or clinical score prediction from cross-sectional imaging. In this work, we propose Disease Continuum Positioning (DCP), a longitudinal Bayesian Learning framework that continuously estimates disease severity from longitudinal diffusion tensor imaging (DTI). Specifically, DCP models disease severity as a low-dimensional probabilistic latent variable by jointly integrating longitudinal observations with weak clinical supervision, from which the proposed Disease Continuum Score (DCS) is derived to quantify an individual's position along the Alzheimer's disease continuum together with its associated uncertainty. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate that DCP consistently outperforms representative disease progression methods. More importantly, comprehensive validation analyses show that DCS accurately characterizes disease severity, exhibits strong clinical relevance, preserves longitudinal disease evolution, and predicts future disease conversion. These results suggest that DCS provides a quantitative imaging-derived representation for continuous assessment of Alzheimer's disease progression beyond conventional diagnostic labels and clinical scores.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Longitudinal Bayesian Learning
Disease Continuum Positioning (DCP)
Disease Continuum Score (DCS)
Alzheimer's Disease
Diffusion Tensor Imaging (DTI)
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