Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation
Abstract Purpose The objective of this study was to develop a quantitatively analyzed Traditional Chinese Medicine (TCM) constitution recognition model utilizing tongue fusion features and deep learning techniques. Methods A prospective investigation was conducted on participants undergoing TCM cons...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | Chinese Medicine |
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| Online Access: | https://doi.org/10.1186/s13020-025-01126-w |
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| author | Yongyue Liu Linmiao Fan Mei Zhao Dongshen Wei Menglan Zhao Yihang Dong Xiaoqing Zhang |
| author_facet | Yongyue Liu Linmiao Fan Mei Zhao Dongshen Wei Menglan Zhao Yihang Dong Xiaoqing Zhang |
| author_sort | Yongyue Liu |
| collection | DOAJ |
| description | Abstract Purpose The objective of this study was to develop a quantitatively analyzed Traditional Chinese Medicine (TCM) constitution recognition model utilizing tongue fusion features and deep learning techniques. Methods A prospective investigation was conducted on participants undergoing TCM constitution assessment at two medical centers. Tongue images and corresponding TCM constitution data were collected from 1374 participants using specialized equipment. Both traditional and deep features were extracted from these images. Significant features associated with constitutional characteristics were identified through LASSO regression and Random Forest (RF). Eight machine learning algorithms were employed to construct and evaluate the efficacy of the models. The highest-performing model was selected as the foundational classifier for developing an integrated tongue image feature model. Model performance was comprehensively evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). Results Analysis revealed 11 critical traditional tongue image features and 26 deep tongue image features. Three datasets were constructed: traditional tongue image features, deep tongue image features, and a fusion feature dataset incorporating both. The multilayer perceptron (MLP) model combining traditional and deep features demonstrated superior performance in TCM constitution classification compared to single-feature models. In the training phase, the model achieved an accuracy (ACC) of 0.893 and an AUC of 0.948. On the test set, it achieved an ACC of 0.837 and an AUC of 0.898, with sensitivity and specificity of 0.680 and 0.930, respectively, indicating excellent generalization ability. Conclusions This study successfully developed an intelligent TCM constitution recognition model that overcomes the limitations of traditional methods and validates the value of tongue images for accurate constitution recognition. |
| format | Article |
| id | doaj-art-d0008ef307cf45efab761f5f52ae9fb3 |
| institution | OA Journals |
| issn | 1749-8546 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Chinese Medicine |
| spelling | doaj-art-d0008ef307cf45efab761f5f52ae9fb32025-08-20T02:06:36ZengBMCChinese Medicine1749-85462025-06-0120112010.1186/s13020-025-01126-wStudy on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigationYongyue Liu0Linmiao Fan1Mei Zhao2Dongshen Wei3Menglan Zhao4Yihang Dong5Xiaoqing Zhang6School of Life Sciences, Beijing University of Chinese MedicineSchool of Life Sciences, Beijing University of Chinese MedicineSchool of Life Sciences, Beijing University of Chinese MedicineSchool of Life Sciences, Beijing University of Chinese MedicineSchool of Life Sciences, Beijing University of Chinese MedicineSchool of Life Sciences, Beijing University of Chinese MedicineSchool of Life Sciences, Beijing University of Chinese MedicineAbstract Purpose The objective of this study was to develop a quantitatively analyzed Traditional Chinese Medicine (TCM) constitution recognition model utilizing tongue fusion features and deep learning techniques. Methods A prospective investigation was conducted on participants undergoing TCM constitution assessment at two medical centers. Tongue images and corresponding TCM constitution data were collected from 1374 participants using specialized equipment. Both traditional and deep features were extracted from these images. Significant features associated with constitutional characteristics were identified through LASSO regression and Random Forest (RF). Eight machine learning algorithms were employed to construct and evaluate the efficacy of the models. The highest-performing model was selected as the foundational classifier for developing an integrated tongue image feature model. Model performance was comprehensively evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). Results Analysis revealed 11 critical traditional tongue image features and 26 deep tongue image features. Three datasets were constructed: traditional tongue image features, deep tongue image features, and a fusion feature dataset incorporating both. The multilayer perceptron (MLP) model combining traditional and deep features demonstrated superior performance in TCM constitution classification compared to single-feature models. In the training phase, the model achieved an accuracy (ACC) of 0.893 and an AUC of 0.948. On the test set, it achieved an ACC of 0.837 and an AUC of 0.898, with sensitivity and specificity of 0.680 and 0.930, respectively, indicating excellent generalization ability. Conclusions This study successfully developed an intelligent TCM constitution recognition model that overcomes the limitations of traditional methods and validates the value of tongue images for accurate constitution recognition.https://doi.org/10.1186/s13020-025-01126-wTCM constitutionTongue diagnosisDeep learningNeural network |
| spellingShingle | Yongyue Liu Linmiao Fan Mei Zhao Dongshen Wei Menglan Zhao Yihang Dong Xiaoqing Zhang Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation Chinese Medicine TCM constitution Tongue diagnosis Deep learning Neural network |
| title | Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation |
| title_full | Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation |
| title_fullStr | Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation |
| title_full_unstemmed | Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation |
| title_short | Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation |
| title_sort | study on a traditional chinese medicine constitution recognition model using tongue image characteristics and deep learning a prospective dual center investigation |
| topic | TCM constitution Tongue diagnosis Deep learning Neural network |
| url | https://doi.org/10.1186/s13020-025-01126-w |
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