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Pan D, Guo Y, Fan Y, Wan H. Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies. THE AMERICAN JOURNAL OF CHINESE MEDICINE 2024; 52:605-623. [PMID: 38715181 DOI: 10.1142/s0192415x24500265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep learning (DL), specifically in computer vision (CV) and natural language processing (NLP), offer novel opportunities to modernize TCM by exploring the profound connotations of its theory. This review begins with an overview of the ML and DL methods employed in TCM; this is followed by practical instances of these applications. Furthermore, extensive discussions emphasize the mature integration of ML and DL in TCM, such as tongue diagnosis, pulse diagnosis, and syndrome differentiation treatment, highlighting their early successful application in the TCM field. Finally, this study validates the accomplishments and addresses the problems and challenges posed by the application and development of TCM powered by ML and DL. As ML and DL techniques continue to evolve, modern technology will spark new advances in TCM.
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Affiliation(s)
- Danping Pan
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Yilei Guo
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Yongfu Fan
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Haitong Wan
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
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Lim J, Li J, Feng X, Feng L, Xia Y, Xiao X, Wang Y, Xu Z. Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis. BMC Complement Med Ther 2023; 23:409. [PMID: 37957660 PMCID: PMC10644435 DOI: 10.1186/s12906-023-04249-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Patients with Polycystic ovary syndrome (PCOS) experienced endocrine disorders that may present vascular function changes. This study aimed to classify and predict PCOS by radial pulse wave parameters using machine learning (ML) methods and to provide evidence for objectifying pulse diagnosis in traditional Chinese medicine (TCM). METHODS A case-control study with 459 subjects divided into a PCOS group and a healthy (non-PCOS) group. The pulse wave parameters were measured and analyzed between the two groups. Seven supervised ML classification models were applied, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forest, Logistic Regression, Voting, and Long Short Term Memory networks (LSTM). Parameters that were significantly different were selected as input features and stratified k-fold cross-validations training was applied to the models. RESULTS There were 316 subjects in the PCOS group and 143 subjects in the healthy group. Compared to the healthy group, the pulse wave parameters h3/h1 and w/t from both left and right sides were increased while h4, t4, t, As, h4/h1 from both sides and right t1 were decreased in the PCOS group (P < 0.01). Among the ML models evaluated, both the Voting and LSTM with ensemble learning capabilities, demonstrated competitive performance. These models achieved the highest results across all evaluation metrics. Specifically, they both attained a testing accuracy of 72.174% and an F1 score of 0.818, their respective AUC values were 0.715 for the Voting and 0.722 for the LSTM. CONCLUSION Radial pulse wave signal could identify most PCOS patients accurately (with a good F1 score) and is valuable for early detection and monitoring of PCOS with acceptable overall accuracy. This technique can stimulate the development of individualized PCOS risk assessment using mobile detection technology, furthermore, gives physicians an intuitive understanding of the objective pulse diagnosis of TCM. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Jiekee Lim
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Jieyun Li
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Xiao Feng
- The First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510405, P. R. China
| | - Lu Feng
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Yumo Xia
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Xinang Xiao
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Yiqin Wang
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
- Shanghai Key Laboratory of Health Identification and Assessment, Shanghai, 201203, P. R. China
| | - Zhaoxia Xu
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China.
- Shanghai Key Laboratory of Health Identification and Assessment, Shanghai, 201203, P. R. China.
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Shi Y, Guo D, Chun Y, Liu J, Liu L, Tu L, Xu J. A lung cancer risk warning model based on tongue images. Front Physiol 2023; 14:1154294. [PMID: 37324390 PMCID: PMC10267397 DOI: 10.3389/fphys.2023.1154294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/12/2023] [Indexed: 06/17/2023] Open
Abstract
Objective: To investigate the tongue image features of patients with lung cancer and benign pulmonary nodules and to construct a lung cancer risk warning model using machine learning methods. Methods: From July 2020 to March 2022, we collected 862 participants including 263 patients with lung cancer, 292 patients with benign pulmonary nodules, and 307 healthy subjects. The TFDA-1 digital tongue diagnosis instrument was used to capture tongue images, using feature extraction technology to obtain the index of the tongue images. The statistical characteristics and correlations of the tongue index were analyzed, and six machine learning algorithms were used to build prediction models of lung cancer based on different data sets. Results: Patients with benign pulmonary nodules had different statistical characteristics and correlations of tongue image data than patients with lung cancer. Among the models based on tongue image data, the random forest prediction model performed the best, with a model accuracy of 0.679 ± 0.048 and an AUC of 0.752 ± 0.051. The accuracy for the logistic regression, decision tree, SVM, random forest, neural network, and naïve bayes models based on both the baseline and tongue image data were 0.760 ± 0.021, 0.764 ± 0.043, 0.774 ± 0.029, 0.770 ± 0.050, 0.762 ± 0.059, and 0.709 ± 0.052, respectively, while the corresponding AUCs were 0.808 ± 0.031, 0.764 ± 0.033, 0.755 ± 0.027, 0.804 ± 0.029, 0.777 ± 0.044, and 0.795 ± 0.039, respectively. Conclusion: The tongue diagnosis data under the guidance of traditional Chinese medicine diagnostic theory was useful. The performance of models built on tongue image and baseline data was superior to that of the models built using only the tongue image data or the baseline data. Adding objective tongue image data to baseline data can significantly improve the efficacy of lung cancer prediction models.
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Affiliation(s)
- Yulin Shi
- Experimental Education Center of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dandan Guo
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yi Chun
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiayi Liu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lingshuang Liu
- Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Liping Tu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiatuo Xu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Tian Z, Wang D, Sun X, Fan Y, Guan Y, Zhang N, Zhou M, Zeng X, Yuan Y, Bu H, Wang H. Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:145. [PMID: 36846009 PMCID: PMC9951008 DOI: 10.21037/atm-22-6431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/13/2023] [Indexed: 02/05/2023]
Abstract
Background With the development of technology and the renewal of traditional Chinese medicine (TCM) diagnostic equipment, artificial intelligence (AI) has been widely applied in TCM. Numerous articles employing this technology have been published. This study aimed to outline the knowledge and themes trends of the four TCM diagnostic methods to help researchers quickly master the hotspots and trends in this field. Four TCM diagnostic methods is a TCM diagnostic method through inspection, listening, smelling, inquiring and palpation, the purpose of which is to collect the patient's medical history, symptoms and signs. Then, it provides an analytical basis for later disease diagnosis and treatment plans. Methods Publications related to AI-based research on the four TCM diagnostic methods were selected from the Web of Science Core Collection, without any restriction on the year of publication. VOSviewer and Citespace were primarily used to create graphical bibliometric maps in this field. Results China was the most productive country in this field, and Evidence-Based Complementary and Alternative Medicine published the largest number of related papers, and the Shanghai University of Traditional Chinese Medicine is the dominant research organization. The Chengdu University of Traditional Chinese Medicine had the highest average number of citations. Jinhong Guo was the most influential author and Artificial Intelligence in Medicine was the most authoritative journal. Six clusters separated by keywords association showed the range of AI-based research on the four TCM diagnostic methods. The hotspots of AI-based research on the four TCM diagnostic methods included the classification and diagnosis of tongue images in patients with diabetes and machine learning for TCM symptom differentiation. Conclusions This study demonstrated that AI-based research on the four TCM diagnostic methods is currently in the initial stage of rapid development and has bright prospects. Cross-country and regional cooperation should be strengthened in the future. It is foreseeable that more related research outputs will rely on the interdisciplinarity of TCM and the development of neural networks models.
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Affiliation(s)
- Zhikui Tian
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Dongjun Wang
- College of Traditional Chinese Medicine, North China University of Science and Technology, Tangshan, China
| | - Xuan Sun
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yadong Fan
- Graduate School, Nanjing University of Chinese Medicine, Nanjing, China;,Institute of Hypertension, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yuanyuan Guan
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Naijin Zhang
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Mi Zhou
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xianyue Zeng
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yin Yuan
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Huaien Bu
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Hongwu Wang
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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Liu X, Huang X, Zhao J, Su Y, Shen L, Duan Y, Gong J, Zhang Z, Piao S, Zhu Q, Rong X, Guo J. Application of machine learning in Chinese medicine differentiation of dampness-heat pattern in patients with type 2 diabetes mellitus. Heliyon 2023; 9:e13289. [PMID: 36873141 PMCID: PMC9975099 DOI: 10.1016/j.heliyon.2023.e13289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 02/15/2023] Open
Abstract
Background China has become the country with the largest number of people with type 2 diabetes mellitus (T2DM), and Chinese medicine (CM) has unique advantages in preventing and treating T2DM, while accurate pattern differentiation is the guarantee for proper treatment. Objective The establishment of the CM pattern differentiation model of T2DM is helpful to the pattern diagnosis of the disease. At present, there are few studies on dampness-heat pattern differentiation models of T2DM. Therefore, we establish a machine learning model, hoping to provide an efficient tool for the pattern diagnosis of CM for T2DM in the future. Methods A total of 1021 effective samples of T2DM patients from ten CM hospitals or clinics were collected by a questionnaire including patients' demographic and dampness-heat-related symptoms and signs. All information and the diagnosis of the dampness-heat pattern of patients were completed by experienced CM physicians at each visit. We applied six machine learning algorithms (Artificial Neural Network [ANN], K-Nearest Neighbor [KNN], Naïve Bayes [NB], Support Vector Machine [SVM], Extreme Gradient Boosting [XGBoost] and Random Forest [RF]) and compared their performance. And then we also utilized Shapley additive explanation (SHAP) method to explain the best performance model. Results The XGBoost model had the highest AUC (0.951, 95% CI 0.925-0.978) among the six models, with the best sensitivity, accuracy, F1 score, negative predictive value, and excellent specificity, precision, and positive predictive value. The SHAP method based on XGBoost showed that slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis. The slippery pulse or rapid-slippery pulse, sticky stool with ungratifying defecation also performed an important role in this diagnostic model. Furthermore, the red tongue acted as an important tongue sign for the dampness-heat pattern. Conclusion This study constructed a dampness-heat pattern differentiation model of T2DM based on machine learning. The XGBoost model is a tool with the potential to help CM practitioners make quick diagnosis decisions and contribute to the standardization and international application of CM patterns.
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Affiliation(s)
- Xinyu Liu
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Xiaoqiang Huang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China
| | - Jindong Zhao
- The First Affiliated Hospital of Anhui University of Chinese, Hefei, 230031, China
| | - Yanjin Su
- Shaanxi University of Chinese Medicine, Xi'an, 712046, China
| | - Lu Shen
- Shaanxi Provincial Hospital of Traditional Chinese Medicine, Xi'an, 710003, China
| | - Yuhong Duan
- Affiliated Hospital of Shannxi University of Chinese Medicine, Xi'an, 712000, China
| | - Jing Gong
- Department of Integrated Traditional Chinese and Western Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zhihai Zhang
- The First Affiliated Hospital of Xiamen University, Xiamen, 361003, China
| | - Shenghua Piao
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Qing Zhu
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Xianglu Rong
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Jiao Guo
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
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Liu Q, Li Y, Yang P, Liu Q, Wang C, Chen K, Wu Z. A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation. Digit Health 2023; 9:20552076231191044. [PMID: 37559828 PMCID: PMC10408356 DOI: 10.1177/20552076231191044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 07/13/2023] [Indexed: 08/11/2023] Open
Abstract
The rapid development of artificial intelligence technology has gradually extended from the general field to all walks of life, and intelligent tongue diagnosis is the product of a miraculous connection between this new discipline and traditional disciplines. We reviewed the deep learning methods and machine learning applied in tongue image analysis that have been studied in the last 5 years, focusing on tongue image calibration, detection, segmentation, and classification of diseases, syndromes, and symptoms/signs. Introducing technical evolutions or emerging technologies were applied in tongue image analysis; as we have noticed, attention mechanism, multiscale features, and prior knowledge were successfully applied in it, and we emphasized the value of combining deep learning with traditional methods. We also pointed out two major problems concerned with data set construction and the low reliability of performance evaluation that exist in this field based on the basic essence of tongue diagnosis in traditional Chinese medicine. Finally, a perspective on the future of intelligent tongue diagnosis was presented; we believe that the self-supervised method, multimodal information fusion, and the study of tongue pathology will have great research significance.
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Affiliation(s)
- Qi Liu
- Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
- Shenzhen Institute of Advanced Technology of the Chinese Academy of Science, Shenzhen, Guangdong, China
| | - Yan Li
- Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
| | - Peng Yang
- Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
| | - Quanquan Liu
- Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
| | - Chunbao Wang
- Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
| | - Keji Chen
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhengzhi Wu
- Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
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Li D, Hu J, Zhang L, Li L, Yin Q, Shi J, Guo H, Zhang Y, Zhuang P. Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine. Eur J Pharmacol 2022; 933:175260. [PMID: 36116517 DOI: 10.1016/j.ejphar.2022.175260] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/15/2022] [Accepted: 09/05/2022] [Indexed: 11/19/2022]
Abstract
It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.
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Affiliation(s)
- Dongna Li
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jing Hu
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Lin Zhang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Lili Li
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Qingsheng Yin
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jiangwei Shi
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China
| | - Hong Guo
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yanjun Zhang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China.
| | - Pengwei Zhuang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
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Zheng L, Mingxue Z, Zeng L, Yushi Z, Yuhan A, Yi Y, Botong L. A Landscape of Metabonomics for Intermingled Phlegm and Blood Stasis and Its Concurrent Syndromes in Stable Angina Pectoris of Coronary Heart Disease. Front Cardiovasc Med 2022; 9:871142. [PMID: 35647058 PMCID: PMC9136041 DOI: 10.3389/fcvm.2022.871142] [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: 02/07/2022] [Accepted: 04/06/2022] [Indexed: 11/30/2022] Open
Abstract
Objectives In this study, we analyzed the metabonomics of intermingled phlegm and blood stasis (IPBS) and its three concurrent syndromes in patients with stable angina pectoris of coronary heart disease. Methods A total of 164 sera of separated outpatients from 12 national tradition Chinese medicine clinical research centers with IPBS or concurrent syndromes were collected for the study and assessed with LC-ESI-MS/MS (liquid chromatography—electrospray ionization tandem—mass spectrometry)-based metabolomics and multivariate statistical analysis. Results Non-differential metabolites between IPBS and its separate syndrome combined with the top 100 most abundant metabolites in four groups were screened to reflect the essence of IPBS. Amino acid and its metabolomics and glycerol phospholipids were screened for common metabolites, and these metabolites were mainly enriched in valine, leucine, and isoleucine metabolism and glycerophospholipid metabolism. Principal component analysis revealed that the difference between IPBS and its separate concurrent syndromes was not distinct. Compared with IPBS, anserine, cytidine 5′-diphosphocholine, and 7,8-dihydro-L-biopterin separately significant increase in phlegm stasis and toxin (PST), phlegm stasis and Qi stagnation (PQS), and phlegm stasis and Qi deficiency (PQD). While these different metabolites were associated with histidine metabolism, beta-alanine metabolism, glycerophospholipid metabolism, and folate biosynthesis. Three accurate identification models were obtained to identify the difference between IPBS and its concurrent syndromes. Conclusion Our study indicated that valine, leucine, and isoleucine metabolism and glycerophospholipid metabolism could represent the essence of IPBS; dysregulated metabolites were valuable in identifying PST from IPBS.
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