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Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndromecharacterized by hepatic steatosis resulting from the exclusion of alcohol andother identifiable liver-damaging factors. It has emerged as a leading cause ofchronic liver disease worldwide. Currently, the conventional methods for NAFLDdetection are expensive and not suitable for users to perform dailydiagnostics. To address this issue, this study proposes a non-invasive andinterpretable NAFLD diagnostic method, the required user-provided indicatorsare only Gender, Age, Height, Weight, Waist Circumference, Hip Circumference,and tongue image. This method involves merging patients' physiologicalindicators with tongue features, which are then input into a fusion networknamed SelectorNet. SelectorNet combines attention mechanisms with featureselection mechanisms, enabling it to autonomously learn the ability to selectimportant features. The experimental results show that the proposed methodachieves an accuracy of 77.22\% using only non-invasive data, and it alsoprovides compelling interpretability matrices. This study contributes to theearly diagnosis of NAFLD and the intelligent advancement of TCM tonguediagnosis. The project mentioned in this paper is currently publicly available.

A Non-Invasive Interpretable NAFLD Diagnostic Method Combining TCM Tongue Features