aceAn expert routing-driven graph framework from tongue multi-task features to liver fibrosis prediction
Liver fibrosis is a global health concern where early detection is crucial for timely intervention. Traditional Chinese Medicine tongue diagnosis offers a promising non-invasive assessment, but suffers from inter-observer variability and subjective interpretation. Current artificial intelligence approaches focus on binary classification without revealing tongue features influencing diagnosis, limiting clinical utility. To address these challenges, we propose two key artificial intelligence innovations: (1) Hiera…
A tongue-image framework that identifies seven tongue attributes and models how they interact reached 0.751 accuracy and 0.767 AUC for liver fibrosis risk screening, with results better than the compared graph and multimodal models.
Accuracy of 0.751 and AUC of 0.767 are moderate, and the source reports no clinical deployment or patient outcomes, so the tool is shown only as a risk screening framework.
Evidence
- Peer-reviewedEngineering Applications of Artificial Intelligence2026-10-14
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Truvace Impact Record TRV-2026-1443, v1: “An expert routing-driven graph framework from tongue multi-task features to liver fibrosis prediction.” Truvace, 2026-10-15. /record/TRV-2026-1443 (accessed at citation time). sha256 7198b626446309c0…
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