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.

翻译：非酒精性脂肪性肝病（NAFLD）是指排除酒精及其他明确肝损伤因素后，以肝细胞脂肪变性为主要特征的临床病理综合征，现已成为全球慢性肝病的主要病因。当前常规NAFLD检测方法成本高昂，不适用于用户日常自我诊断。为解决该问题，本研究提出一种非侵入性且具有可解释性的NAFLD诊断方法，所需用户提供的指标仅为性别、年龄、身高、体重、腰围、臀围及舌象图像。该方法将患者生理指标与舌象特征融合后，输入名为SelectorNet的融合网络。SelectorNet结合注意力机制与特征选择机制，能够自主学习重要特征筛选能力。实验结果表明，该方法仅使用非侵入性数据即可实现77.22%的准确率，并提供了具有说服力的可解释性矩阵。本研究有助于NAFLD的早期诊断及中医舌诊的智能化发展。本文项目代码可见：https://github.com/cshan-github/SelectorNet