AI-driven multimodal fusion of tongue images and clinical indicators for identifying MAFLD patients at risk of coronary artery disease: An exploratory study
Our AI-driven dual-model framework addresses the critical unmet need for CAD identification in MAFLD patients, providing a community-scalable screening tool (Model-1) and a precision clinical assessment model (Model-2), while offering empirical support for TCM tongue diagnosis. Pending external validation to confirm its generalizability, this approach may serve as a cost-efficient non-invasive screening strategy for this high-risk population.
Authors
- Zhaoyun He
- Jian Xue (ORCID: https://orcid.org/0000-0002-4337-1704)
- Chao Zhou (ORCID: https://orcid.org/0000-0003-1785-3969)
- Shuangnan Fu
- Jingjing Zhang
- Ning Zhang (ORCID: https://orcid.org/0000-0002-6826-9175)
- Pengcheng Liu (ORCID: https://orcid.org/0000-0002-9144-1623)
- Song Feng
- Shuang-Nan Zhou (ORCID: https://orcid.org/0009-0000-4570-5205)
- Man Gong
- Hui Feng (ORCID: https://orcid.org/0009-0001-1716-0436)
Institutions
- Chinese PLA General Hospital (CN)
Publication Details
- Journal
- iLiver
- Published
- 2025-08-08
- DOI
- https://doi.org/10.1016/j.iliver.2025.100181
- Citations
- 1
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Natural Science Foundation of Beijing Municipality