Liver fibrosis is a reversible precursor to cirrhosis, and early detection is key to halting disease progression. Tongue diagnosis provides a non-invasive and cost-effective insight into internal health; however, its subjectivity limits clinical reliability. We developed TongVMoe, a multi-task deep learning model trained on 2202 tongue images from 1601 patients, to detect liver fibrosis and simultaneously classify seven key tongue features. The model achieved an area under the curve (AUC) of 0.8061, outperforming State-of-the-Art methods such as DiffMIC-v2 (0.6929), HorNet (0.7018), InceptionNeXt (0.7012), LSNet (0.6971), and TransXNet (0.7062). TongVMoe also demonstrated robust recognition of tongue features, with AUCs of 0.9752 for cracks and 0.9232 for greasy coating. Among these features, petechiae emerged as a significant clinical indicator, showing a strong correlation with liver fibrosis (χ² = 19.516, P < 0.001). We further integrated the model into a WeChat mini-program and simulated remote screening, achieving an accuracy of 77.8% and a sensitivity of 86.2%. These findings suggest that the TongVMoe has the potential to serve as an interpretable and mobile-compatible tool for the early detection and monitoring of liver fibrosis, particularly in resource-limited areas. Trial registration: Chinese Clinical Trial Registry (ChiCTR2100053676, registered 27 November 2021).
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