用机器学习建模阿尔茨海默病患者特异的脑部生物标志物演变，预测更准。
Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression
- 通过算子学习直接捕捉每位患者的疾病演化规律。
- 在多个生物标志物上预测准确率超90%，显著优于现有方法。
- 适合精准医疗、神经退行性疾病研究者参考。
阿尔茨海默病（AD）是一种复杂且异质性显著的神经退行性疾病，其进展和治疗反应差异大。尽管近年治疗取得进展，但能准确预测个体化疾病轨迹的模型仍有限。本文提出一种基于机器学习的算子学习框架，整合纵向多模态影像、生物标志物及临床数据，实现个性化AD进展建模。不同于预设动力学的传统模型，本方法直接学习驱动淀粉样蛋白、tau蛋白和神经退行性变等生物标志物时空演化的患者特异性算子。利用拉普拉斯特征函数基，构建具备几何感知能力的神经算子，以捕捉复杂的脑部动态。嵌入数字孪生范式后，该框架支持个体化预测、治疗干预模拟及虚拟临床试验。应用于真实临床数据，方法在多个生物标志物上的预测准确率超过90%，显著优于现有方法。本工作为神经退行性疾病提供了一个可扩展、可解释的精准建模与个性化治疗优化平台。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a complex, multifactorial neurodegenerative disorder with substantial heterogeneity in progression and treatment response. Despite recent therapeutic advances, predictive models capable of accurately forecasting individualized disease trajectories remain limited. Here, we present a machine learning-based operator learning framework for personalized modeling of AD progression, integrating longitudinal multimodal imaging, biomarker, and clinical data. Unlike conventional models with prespecified dynamics, our approach directly learns patient-specific disease operators governing the spatiotemporal evolution of amyloid, tau, and neurodegeneration biomarkers. Using Laplacian eigenfunction bases, we construct geometry-aware neural operators capable of capturing complex brain dynamics. Embedded within a digital twin paradigm, the framework enables individualized predictions, simulation of therapeutic interventions, and in silico clinical trials. Applied to AD clinical data, our method achieves high prediction accuracy exceeding 90% across multiple biomarkers, substantially outperforming existing approaches. This work offers a scalable, interpretable platform for precision modeling and personalized therapeutic optimization in neurodegenerative diseases.
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