Clinically Informed Adaptive Multimodal Graph Learning Paradigm for Transparent Temporal and Generalizable Alzheimer’s Disease Diagnosis

This is a clinically reliable and explainable diagnostic framework for the early detection of Alzheimer's disease with multimodal data. Current computational methods face challenges in dealing with fragmented clinical information, poor cross-modal integration, limited temporal modelling, and low interpretability, rendering them unsuitable for real-world medical deployment. To overcome these limitations, we propose the Clinically Guided Adaptive Multimodal Graph Transformer (CAM-GT), a novel architecture that fuses clinical priors with graph-based learning and transformer-driven temporal reasoning within a unified model. The proposed framework uniquely integrates clinically guided graph attention, cross-modal fusion, and contrastive alignment, where the system can capture hidden relationships among imaging, cognitive scores, and clinical biomarkers with high robustness against missing or imbalanced modalities. Implemented on the Python platform with advanced deep-learning libraries, CAM-GT carries out multimodal encoding, temporal progression modeling, and explainability mapping in order to identify the most significant biomarkers that influence the status of a disease. Experimental evaluation demonstrates that the model performs well by achieving an accuracy of 97%, a 97.2% AUC, and outperforming existing models while maintaining strong generalization in heterogeneous clinical environments. Further, high interpretability ensures that clinically, it will be able to trace how predictions are made to instill greater trust and ethical reliability and increase the adoption potential in hospitals and research centers. Finally, CAM-GT benefits neurologists, radiologists, healthcare institutions, and researchers by providing a stable, transparent, high-performing AI system that has the capability to support early diagnosis and guide real-world clinical decision-making in neurodegenerative disease care.

@article{Shrivastava2025,
  title     = {Clinically Informed Adaptive Multimodal Graph Learning Paradigm for Transparent Temporal and Generalizable Alzheimer’s Disease Diagnosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Padmavati Shrivastava and V S Krushnasamy and Guru Basava Aradhya S and Vinod Waiker and Peddireddy Veera Venkateswara Rao and Elangovan Muniyandy and Khaled Bedair},
  doi       = {10.14569/IJACSA.2025.0161173},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161173}
}