Toward Personalized Digital Twins for Cognitive Decline Assessment: A Multimodal, Uncertainty-Aware Framework

arXiv cs.AI / 5/1/2026

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Key Points

The paper proposes the Personalized Cognitive Decline Assessment Digital Twin (PCD-DT), a multimodal, uncertainty-aware framework to model individual patient disease trajectories from sparse and irregular longitudinal data.
PCD-DT integrates latent state-space models for personalized temporal dynamics, multimodal fusion of clinical/biomarker/imaging signals, and uncertainty-aware validation with adaptive updating to improve robustness.
It outlines how conditional generative models can enable data augmentation and stress testing for progression patterns that are underrepresented in training data.
In a feasibility study using longitudinal TADPOLE data, the approach shows strong separation between cognitively normal and Alzheimer’s cohorts over five years across measures including ADAS13, ventricle volume, and hippocampal volume.
A multimodal next-visit prediction experiment using an LSTM on 3,003 visit-pair sequences finds that combining cognitive plus MRI features yields the lowest RMSE (e.g., ADAS13: 0.4419; ventricle volume: 0.5842), outperforming a Last Observation Carried Forward baseline, while emphasizing the need for better uncertainty calibration and longer-horizon evaluation.

Abstract

Cognitive decline is highly heterogeneous across individuals, which complicates prognosis, trial design, and treatment planning. We present the Personalized Cognitive Decline Assessment Digital Twin (PCD-DT), a multimodal and uncertainty-aware framework for modeling patient-specific disease trajectories from sparse, noisy, and irregular longitudinal data. The framework combines three methodological components: (1) latent state-space models for individualized temporal dynamics, (2) multimodal fusion for clinical, biomarker, and imaging features, and (3) uncertainty-aware validation and adaptive updating for robust digital twin operation. We also outline how conditional generative models can support data augmentation and stress testing for underrepresented progression patterns. As a preliminary feasibility study, we analyze longitudinal TADPOLE trajectories and show clear separation between cognitively normal and Alzheimer's disease cohorts in ADAS13, ventricle volume, and hippocampal volume over five years. We further conduct a multimodal next-visit prediction ablation using an LSTM sequence model on 3{,}003 visit-pair sequences derived from TADPOLE, where the combined cognitive plus MRI configuration achieves the lowest standardized RMSE for both ADAS13 (0.4419) and ventricle volume (0.5842), outperforming a Last Observation Carried Forward baseline. A Bayesian tensor modeling component for high-dimensional imaging fusion is also discussed. These results support the feasibility of the proposed architecture while also highlighting the need for stronger uncertainty calibration and longer-horizon predictive evaluation. The PCD-DT framework provides a principled starting point for personalized in silico modeling in neurodegenerative disease. This work positions PCD-DT as a foundational step toward clinically deployable, uncertainty-aware digital twin systems.