Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer’s Disease Continuum

阿尔茨海默病连续谱中疾病位置的纵向贝叶斯学习

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.

摘要： 阿尔茨海默病（AD）是一个连续的生物学进展过程，然而目前大多数基于神经影像的人工智能方法仍局限于横断面影像的离散诊断或临床评分预测。

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).

在这项工作中，我们提出了疾病连续谱定位（DCP），这是一个纵向贝叶斯学习框架，能够通过纵向弥散张量成像（DTI）持续评估疾病的严重程度。

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.

具体而言，DCP 通过将纵向观测数据与弱临床监督相结合，将疾病严重程度建模为低维概率潜变量，并由此推导出疾病连续谱评分（DCS），用于量化个体在阿尔茨海默病连续谱中的位置及其相关的不确定性。

Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort demonstrate that DCP consistently outperforms representative disease progression methods.

在阿尔茨海默病神经影像学倡议（ADNI）队列上进行的广泛实验表明，DCP 的表现始终优于现有的代表性疾病进展评估方法。

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.

更重要的是，综合验证分析显示，DCS 能够准确表征疾病严重程度，展现出强大的临床相关性，保留了疾病的纵向演变特征，并能预测未来的疾病转化。

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.

这些结果表明，DCS 提供了一种基于影像的定量表征，能够超越传统的诊断标签和临床评分，实现对阿尔茨海默病进展的持续评估。