Multi-Modal Fusion with Supervised Contrastive Learning Model for Early Alzheimer’s Disease Diagnosis and Multi-Modal Biomarker Identification

Key Points

• Founded on multi-modal fusion, the study identifies biomarkers critical for early Alzheimer’s disease diagnosis.
• Using supervised contrastive learning, the model demonstrates enhanced accuracy in distinguishing early disease stages with greater than 85% sensitivity.
• Analysis utilizes neural networks across various data types, including imaging and genetic biomarkers, to validate effectiveness.
• Highlights the pressing need for early detection strategies, as timely intervention could significantly improve patient outcomes.