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MIRAGE: Knowledge Graph-Guided Cross-Cohort MRI Synthesis for Alzheimer's Disease Prediction

arXiv – CS AI|Guanchen Wu, Zhe Huang, Yuzhang Xie, Runze Yan, Akul Chopra, Deqiang Qiu, Xiao Hu, Fei Wang, Carl Yang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce MIRAGE, a novel AI framework that uses knowledge graphs and electronic health records to predict Alzheimer's disease when MRI scans are unavailable. The system improves AD classification rates by 13% compared to single-modality approaches by creating synthetic representations without expensive 3D brain scan reconstruction.

Key Takeaways

→MIRAGE addresses the critical problem of missing MRI data in Alzheimer's diagnosis by leveraging electronic health records and knowledge graphs.
→The framework uses Graph Attention Networks to create unified embeddings from heterogeneous patient data across different cohorts.
→A frozen 3D U-Net decoder serves as regularization to ensure biologically plausible representations without actual voxel reconstruction.
→The system achieved 13% improvement in AD classification accuracy compared to unimodal baselines in cohorts without MRI scans.
→This approach could make AI-powered Alzheimer's diagnosis more accessible by reducing dependency on expensive imaging equipment.

#alzheimers #medical-ai #knowledge-graphs #mri-synthesis #healthcare #multimodal-ai #graph-attention-networks #disease-prediction

Read Original →via arXiv – CS AI

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