Noninvasive Diagnostic Technique for Nonalcoholic Fatty Liver Disease Based on Features of Tongue Images.
10.1007/s11655-023-3616-1
- Author:
Rong-Rui WANG
1
;
Jia-Liang CHEN
2
;
Shao-Jie DUAN
1
;
Ying-Xi LU
3
;
Ping CHEN
4
;
Yuan-Chen ZHOU
5
;
Shu-Kun YAO
6
Author Information
1. Graduate School of Beijing University of Chinese Medicine, Beijing, 100029, China.
2. Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
3. Nanjing Linkwah Micro-electronics Institute, Beijing, 100191, China.
4. Institute of Microelectronics, Tsinghua University, Beijing, 100084, China.
5. Peking University China-Japan Friendship School of Clinical Medicine, Beijing, 100029, China.
6. Graduate School of Beijing University of Chinese Medicine, Beijing, 100029, China. shukunyao@126.com.
- Publication Type:Journal Article
- Keywords:
Chinese medicine;
machine learning;
nonalcoholic fatty liver disease;
noninvasive diagnosis;
tongue diagnosis;
tongue image
- MeSH:
Humans;
Non-alcoholic Fatty Liver Disease/diagnostic imaging*;
Ultrasonography;
Anthropometry;
Algorithms;
China
- From:
Chinese journal of integrative medicine
2024;30(3):203-212
- CountryChina
- Language:English
-
Abstract:
OBJECTIVE:To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images.
METHODS:Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD.
RESULTS:A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68-0.98]. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66-0.92) in validation set.
CONCLUSIONS:The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.
