Adaptive Neuro-Digital Twin with Cross-Domain Multimodal Representation Learning for Early Alzheimer's Disease Prognosis

Alzheimer's disease (AD) refers to a progressive neurodegenerative disease involving cognitive impairment, brain atrophy, and functional neurological deficits that make early prediction and subsequent disease progression monitoring extremely difficult. Currently available AI methods predominantly focus on employing single modality analysis or static multimodal analysis approaches, which tend to solve AD prognosis as a binary classification problem. Also, much of the current literature on AD does not take into account the importance of progression-aware, patient-wise evaluation, and robust management of multimodality data heterogeneity or missingness. To tackle such issues, this study presents the proposal of an Adaptive Neuro-Digital Twin-based framework for predicting Alzheimer's disease, known as ANDT-AD. The proposed framework incorporates the heterogeneous multimodality information in clinical-cognitive data, structural Magnetic Resonance Imaging (MRI), and Electroencephalography (EEG) signals through modality-wise deep encoders. Specifically, a clinical encoder based on a transformer structure is utilized to capture non-linear cognitive interactions, while a Vision Transformer model and an attention-enhanced temporal EEG encoder model help to extract neuroanatomical information from MRI signals and electrophysiological signals in EEG, respectively. The framework is developed using Python and assessed using public domain clinical, MRIs, and OpenNeuro ds004504 EEG data sets for five-fold cross-validation and patient-level evaluations. Experimentation yielded 98.0% diagnostic accuracy with an AUC of 0.97, which exceeds the performance of current multimodal baselines. Moreover, the framework attained an MAE of 1.12, an RMSE of 1.46, and a progression risk C index of 0.89, proving its strength in predicting cognitive decline and personalizing disease progression models under heterogeneity and missingness of multimodal scenarios.

@article{Krushnasamy2026,
  title     = {Adaptive Neuro-Digital Twin with Cross-Domain Multimodal Representation Learning for Early Alzheimer's Disease Prognosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {V S Krushnasamy and Annapurna Mishra and Pratik Gite and Ganesh Kumar Anbazhagan and Adapa Gopi and M.Misba and A. Arul Anitha and Osama R.Shahin},
  doi       = {10.14569/IJACSA.2026.0170659},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170659}
}