cLDM-ODE: A Multimodal Generative Framework for Uncertainty-Aware Forecasting of Alzheimer's Disease Progression

Context The imminent performance of multimodal and heterogeneous modalities to forecast the advance of Alzheimer's disease (AD) precisely is one of the key problems. Current models are usually not interpretable, time-consistent, and multimodal, making them less useful in clinical forecasting. Objective The objective of the study is to develop a hybrid generative approach to simulate the individualized AD progression process, which can generate future anatomical and clinical states, model latent over-time dynamics, and measure the uncertainty. Methods The proposed study suggests using a multimodal paradigm that enables a combination of Conditional Latent Diffusion Models (cLDM) and Neural Ordinary Differential Equations (ODEs). The model permits the generation of plausible future MRI, cognitive scoring, and biomarker trajectories for a patient at baseline. The ADNI dataset was evaluated with structural similarity (SSIM), clinical prediction error, and classification accuracy. Key Findings The model provided an SSIM equal to 0.86 on synthesizing future MRI, and the MAE of MMSE prediction was equal to 1.5. It exceeded baselines in all the imaging, cognitive, and biomarker settings. The conversion of AD resulted in an accuracy of the classification of 88% with stable multimodal generalization at calibrated output of probability. Conclusion The proposed model offers a feasible and explainable approach to the forecast of an AD trajectory, allowing realistic simulations of a digital twin and projecting its progress within a multi-year perspective. It also supports early detection, custom intervention, and uncertainty-conscious clinical decision-making.