Irfan Alam

Parkinson's disease diagnosis still rests on symptoms and expert assessment, while multimodal clinical data are heterogeneous, labels are scarce, and deep models rarely report calibrated confidence. NeuroQ-MGST encodes voice, gait, neuroimaging and clinical variables with spiking neural networks, aligns them through self-supervised contrastive and masked reconstruction objectives, reasons over a dynamically rebuilt heterogeneous graph with a hierarchical graph transformer, and adds quantum-inspired variational refinement plus Monte Carlo dropout uncertainty. On the PMMD 2.0 test set it reaches 98.7% accuracy and 0.989 AUC-ROC, and 95.8% zero-shot accuracy on an external European cohort without fine-tuning.

Parkinson's disease diagnosis still rests on symptoms and expert assessment, while multimodal clinical data are heterogeneous, labels are scarce, and deep models rarely report calibrated confidence. NeuroQ-MGST encodes voice, gait, neuroimaging and clinical variables with spiking neural networks, aligns them through self-supervised contrastive and masked reconstruction objectives, reasons over a dynamically rebuilt heterogeneous graph with a hierarchical graph transformer, and adds quantum-inspired variational refinement plus Monte Carlo dropout uncertainty. On the PMMD 2.0 test set it reaches 98.7% accuracy and 0.989 AUC-ROC, and 95.8% zero-shot accuracy on an external European cohort without fine-tuning.