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February 3, 2026Interdisciplinary Sciences Computational Life Sciences

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

Xiaofeng Xie Peng Xue Yihao Guo Yi Lu Li Fan Rongnian Tang Zhenkai Xu Xuanqi Wang Tao Liu Feng Chen

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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.

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Implication

Observational analysis exposes novel biomarkers for early diagnosis of Alzheimer’s disease, indicating potential for improved detection strategies.

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Cite This Study

Xie et al. (2026) studied this question.

synapsesocial.com/papers/69a760c8c6e9836116a2dd9a https://doi.org/10.1007/s12539-025-00805-4

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