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Chang WH, Chen CC, Wu HK, Hsu PC, Lo LC, Chu HT, Chang HH. Tongue feature dataset construction and real-time detection. PLoS One 2024; 19:e0296070. [PMID: 38452007 PMCID: PMC10919637 DOI: 10.1371/journal.pone.0296070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 12/04/2023] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Tongue diagnosis in traditional Chinese medicine (TCM) provides clinically important, objective evidence from direct observation of specific features that assist with diagnosis. However, the current interpretation of tongue features requires a significant amount of manpower and time. TCM physicians may have different interpretations of features displayed by the same tongue. An automated interpretation system that interprets tongue features would expedite the interpretation process and yield more consistent results. MATERIALS AND METHODS This study applied deep learning visualization to tongue diagnosis. After collecting tongue images and corresponding interpretation reports by TCM physicians in a single teaching hospital, various tongue features such as fissures, tooth marks, and different types of coatings were annotated manually with rectangles. These annotated data and images were used to train a deep learning object detection model. Upon completion of training, the position of each tongue feature was dynamically marked. RESULTS A large high-quality manually annotated tongue feature dataset was constructed and analyzed. A detection model was trained with average precision (AP) 47.67%, 58.94%, 71.25% and 59.78% for fissures, tooth marks, thick and yellow coatings, respectively. At over 40 frames per second on a NVIDIA GeForce GTX 1060, the model was capable of detecting tongue features from any viewpoint in real time. CONCLUSIONS/SIGNIFICANCE This study constructed a tongue feature dataset and trained a deep learning object detection model to locate tongue features in real time. The model provided interpretability and intuitiveness that are often lacking in general neural network models and implies good feasibility for clinical application.
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Affiliation(s)
- Wen-Hsien Chang
- Graduate Institute of Chinese Medicine, School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China
| | - Chih-Chieh Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan, Republic of China
| | - Han-Kuei Wu
- School of Post-Baccalaureate Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China
- Department of Traditional Chinese Medicine, Kuang Tien General Hospital, Taichung, Taiwan, Republic of China
| | - Po-Chi Hsu
- Department of Traditional Chinese Medicine, Kuang Tien General Hospital, Taichung, Taiwan, Republic of China
- School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China
| | - Lun-Chien Lo
- School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China
- Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan, Republic of China
| | - Hsueh-Ting Chu
- Department of Computer Science and Information Engineering, College of Computer Science, Asia University, Taichung, Taiwan, Republic of China
| | - Hen-Hong Chang
- Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan, Republic of China
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, and Chinese Medicine Research Center, China Medical University, Taichung, Taiwan, Republic of China
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Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans. Sci Rep 2022; 12:186. [PMID: 34996986 PMCID: PMC8741765 DOI: 10.1038/s41598-021-03879-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 12/06/2021] [Indexed: 12/29/2022] Open
Abstract
Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest. In this research study, a Traditional Chinese Medicine based diabetes diagnosis is presented based on analyzing the extracted features of panoramic tongue images such as color, texture, shape, tooth markings and fur. The feature extraction is done by Convolutional Neural Network (CNN)—ResNet 50 architecture, and the classification is performed by the proposed Deep Radial Basis Function Neural Network (RBFNN) algorithm based on auto encoder learning mechanism. The proposed model is simulated in MATLAB environment and evaluated with performance metrics—accuracy, precision, sensitivity, specificity, F1 score, error rate, and receiver operating characteristics (ROC). On comparing with existing models, the proposed CNN based Deep RBFNN machine learning classifier model outperformed with better classification performance and proving its effectiveness.
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Jiang T, Guo XJ, Tu LP, Lu Z, Cui J, Ma XX, Hu XJ, Yao XH, Cui LT, Li YZ, Huang JB, Xu JT. Application of computer tongue image analysis technology in the diagnosis of NAFLD. Comput Biol Med 2021; 135:104622. [PMID: 34242868 DOI: 10.1016/j.compbiomed.2021.104622] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/28/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882-0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.
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Affiliation(s)
- Tao Jiang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China.
| | - Xiao-Jing Guo
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China.
| | - Li-Ping Tu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Zhou Lu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Ji Cui
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Xu-Xiang Ma
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Xiao-Juan Hu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Xing-Hua Yao
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Long-Tao Cui
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Yong-Zhi Li
- China Astronaut Training Center, Beijing, 100084, China
| | - Jing-Bin Huang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China.
| | - Jia-Tuo Xu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China.
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