阿尔茨海默病连续体中疾病位置的纵向贝叶斯学习

中文摘要 AI 辅助

阿尔茨海默病（AD）是一个连续的生物学进展过程，而大多数现有的基于神经影像的人工智能方法仍局限于从横断面影像进行离散诊断或临床评分预测。本研究提出了疾病连续体定位（Disease Continuum Positioning, DCP），这是一种纵向贝叶斯学习框架，可从纵向扩散张量成像（DTI）中连续估计疾病严重程度。具体而言，DCP通过联合整合纵向观测数据与弱临床监督，将疾病严重程度建模为低维概率潜在变量，从中推导得到疾病连续体评分（Disease Continuum Score, DCS），以量化个体在阿尔茨海默病连续体中的位置及其相关不确定性。在阿尔茨海默病神经影像倡议（ADNI）队列上开展的大量实验表明，DCP始终优于代表性疾病进展方法。更重要的是，综合验证分析显示，DCS能准确表征疾病严重程度、表现出强临床相关性、保留纵向疾病演化特征并预测未来疾病转化。这些结果表明，DCS提供了一种基于影像的定量表示，用于阿尔茨海默病进展的连续评估，超越了传统诊断标签和临床评分的局限。

英文摘要

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.

发表机构

• University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）
• University of Pittsburgh（匹兹堡大学）
• Eli and Lilly Company（礼来公司）
• Washington University in St. Louis（圣路易斯华盛顿大学）

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