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Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression
Jindong Wang1, Yutong Mao2, Xiao Liu2
1Department of Mathematics, Penn State University, University Park, PA, USA.
Arxiv
|July 31, 2025
Summary
This study introduces a novel machine learning framework for personalized Alzheimer's disease (AD) progression modeling. The approach accurately predicts individual patient trajectories using multimodal data, enabling precision medicine for neurodegenerative disorders.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Biomedical Informatics
Background:
- Alzheimer's disease (AD) is a heterogeneous neurodegenerative disorder with limited predictive models for individual progression.
- Accurate forecasting of disease trajectories is crucial for personalized treatment strategies.
Purpose of the Study:
- To develop a machine learning framework for personalized modeling of Alzheimer's disease progression.
- To integrate longitudinal multimodal data for enhanced predictive accuracy.
Main Methods:
- Utilized a machine learning-based operator learning framework.
- Integrated longitudinal multimodal imaging, biomarker, and clinical data.
- Employed Laplacian eigenfunction bases for geometry-aware neural operators within a digital twin paradigm.
Main Results:
- Achieved prediction accuracy exceeding 90% across multiple Alzheimer's disease biomarkers.
- Demonstrated superior performance compared to existing predictive approaches.
- Enabled individualized predictions and simulation of therapeutic interventions.
Conclusions:
- The developed framework offers a scalable and interpretable platform for precision modeling in neurodegenerative diseases.
- This approach facilitates personalized therapeutic optimization for Alzheimer's disease patients.
- The digital twin paradigm supports in silico clinical trials for accelerated drug development.
