Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by hepatic steatosis resulting from the exclusion of alcohol and other identifiable liver-damaging factors. It has emerged as a leading cause of chronic liver disease worldwide. Currently, the conventional methods for NAFLD detection are expensive and not suitable for users to perform daily diagnostics. To address this issue, this study proposes a non-invasive and interpretable NAFLD diagnostic method, the required user-provided indicators are only Gender, Age, Height, Weight, Waist Circumference, Hip Circumference, and tongue image. This method involves merging patients' physiological indicators with tongue features, which are then input into a fusion network named SelectorNet. SelectorNet combines attention mechanisms with feature selection mechanisms, enabling it to autonomously learn the ability to select important features. The experimental results show that the proposed method achieves an accuracy of 77.22\% using only non-invasive data, and it also provides compelling interpretability matrices. This study contributes to the early diagnosis of NAFLD and the intelligent advancement of TCM tongue diagnosis. The project in this paper is available at: https://github.com/cshan-github/SelectorNet.

该研究提出了一种非侵入性、可理解的非酒精性脂肪肝病诊断方法，将患者的生理指标与舌部特征相结合，通过自动学习选择重要特征的能力，实现了77.22％的准确度，提供了令人信服的解释能力矩阵。

一种非侵入性可解释的中医舌象结合NAFLD诊断方法