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

TL;DR

This paper introduces a non-invasive, interpretable diagnostic method for NAFLD that combines physiological data and tongue features using a novel neural network, achieving over 77% accuracy.

Contribution

It presents a fusion network, SelectorNet, that autonomously selects important features from non-invasive data for NAFLD diagnosis, enhancing interpretability and early detection.

Findings

Achieved 77.22% accuracy with non-invasive data

Demonstrated effective feature selection and interpretability

Contributed to early NAFLD diagnosis and TCM tongue analysis

Abstract

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 mentioned in this paper is currently publicly available.