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Wang S, Fang R, Huang L, Zhou L, Liu H, Cai M, Sha’aban A, Yu C, Akkaif MA. Acupuncture in Traditional Chinese Medicine: A Complementary Approach for Cardiovascular Health. J Multidiscip Healthc 2024; 17:3459-3473. [PMID: 39050695 PMCID: PMC11268752 DOI: 10.2147/jmdh.s476319] [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: 05/23/2024] [Accepted: 06/21/2024] [Indexed: 07/27/2024] Open
Abstract
Cardiovascular diseases (CVDs) are increasingly prevalent in clinical settings. With the continuous improvement of people's living standards, the gradual acceleration of the pace of life, and the deterioration of the living environment in recent years, the incidence of CVDs is increasing annually. The prevalence of CVDs among individuals aged 50 and above is notably elevated, posing a significant risk to patients' well-being and lives. At this juncture, numerous clinical treatment choices are available for managing CVDs, with traditional Chinese medicine (TCM) therapy standing out as a practical, safe, and reliable option. Over the recent years, there has been growing acknowledgement among both medical professionals and patients. With the expanding integration of TCM in the treatment of various clinical conditions, the use of TCM in managing CVDs has gained significant attention within the medical community, potentially emerging as an efficacious approach for addressing cardiovascular diseases. This article conducts a comprehensive review of the TCM approach, particularly acupuncture, as a supplementary treatment for CVDs, highlighting its ability to effectively lower blood pressure, decrease coronary artery events, mitigate arrhythmias, and enhance cardiac function when used alongside conventional medication. The review underscores the promise of acupuncture in enhancing cardiovascular health, although variations in research methodologies necessitate standardized applications.
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Affiliation(s)
- Shengfeng Wang
- Rehabilitation Department of Chinese Medicine, The Fourth People’s Hospital of Kunshan, Kunshan City, Jiangsu Province, People’s Republic of China
| | - Ruxue Fang
- Rehabilitation Department of Chinese Medicine, The Fourth People’s Hospital of Kunshan, Kunshan City, Jiangsu Province, People’s Republic of China
| | - Lei Huang
- Rehabilitation Department of Chinese Medicine, The Fourth People’s Hospital of Kunshan, Kunshan City, Jiangsu Province, People’s Republic of China
| | - Liping Zhou
- Rehabilitation Department of Chinese Medicine, The Fourth People’s Hospital of Kunshan, Kunshan City, Jiangsu Province, People’s Republic of China
- Department of Community Health, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Pulau Pinang, Malaysia
| | - Haibo Liu
- Department of Cardiology, QingPu Branch of Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
| | - Meiling Cai
- Department of Obstetrics and Gynecology, Qingpu Branch, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
| | - Abubakar Sha’aban
- Health and Care Research Wales Evidence Centre, Cardiff University, Heath Park, Cardiff, UK
| | - Chunxiang Yu
- Rehabilitation Department of Chinese Medicine, The Fourth People’s Hospital of Kunshan, Kunshan City, Jiangsu Province, People’s Republic of China
| | - Mohammed Ahmed Akkaif
- Department of Cardiology, QingPu Branch of Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
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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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Fatangare M, Bhingarkar S. A comprehensive review on technological advancements for sensor-based Nadi Pariksha: An ancient Indian science for human health diagnosis. J Ayurveda Integr Med 2024; 15:100958. [PMID: 38815517 PMCID: PMC11166873 DOI: 10.1016/j.jaim.2024.100958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/11/2023] [Accepted: 04/24/2024] [Indexed: 06/01/2024] Open
Abstract
Nadi Pariksha is a significant, rather symbolic term for Ayurveda. Ancient Ayurvedic literature has prominently stated its importance in the judgment of Tridoshas (Vata, Pitta, and Kapha) which are the base of ailment diagnosis and prediction. The knowledge about Nadi Pariksha is uncovered in various ancient Ayurvedic literature like Ravansamhita, Bhavprakash, Nadivigyan by Kanad, Sharangdhar, and Yogratnakar. The various Nadi parameters are indicative of the diagnosis of diseases. These techniques were used as popular diagnostic tools in Indian culture from ancient days. Still, nowadays, these are not being used explicitly due to the lack of expertise, so it is necessary to establish their results once gained so that they can be used along with technical aspects in today's era. Ayurveda believes that all the elements of the Universe are present in any human body in minute, proportionate quantity, and the Nadi represents these elements, that is, Vata, Pitta, and Kapha (VPK). To facilitate the Nadi Pariksha using appropriate sensors may help the Ayurveda practitioners diagnose Prakriti and predict some diseases, making the Nadi Pariksha more reliable and faster. This review paper lists, 2 books and 67 research papers, mostly from countries like India, China, Japan, Korea, etc., from various reputed databases. The review primarily concentrates on six research themes: sensors and devices used for Nadi signal acquisition, signal pre-processing methods, feature extraction methods, feature selection approaches, classification practices, diseases diagnosed, and results attained. The paper also reviews the challenges in implementing the automated Nadi Pariksha with technological aid, which is a necessity of this period and is a very vibrant research arena. Yet significant work remains to be done, like bridging the gaps between technical and commercial development, and the procedure standardization is also required.
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Affiliation(s)
- Mrunal Fatangare
- School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India.
| | - Sukhada Bhingarkar
- School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India
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Tian D, Chen W, Xu D, Xu L, Xu G, Guo Y, Yao Y. A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation. Comput Biol Med 2024; 170:108074. [PMID: 38330826 DOI: 10.1016/j.compbiomed.2024.108074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 12/15/2023] [Accepted: 01/27/2024] [Indexed: 02/10/2024]
Abstract
Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors' experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis.
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Affiliation(s)
- Dingcheng Tian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, China
| | - Weihao Chen
- Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China
| | - Dechao Xu
- Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China
| | - Lisheng Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, China
| | - Gang Xu
- The First Affiliated Hospital of Liaoning University of TraditionalChinese Medicine, Shenyang, 110000, China
| | - Yaochen Guo
- The Affiliated Hospital of the Medical School of Ningbo University, Ningbo, 315020, China
| | - Yudong Yao
- Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China.
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Lu H, Huang L, Xie Y, Zhou Z, Cui H, Jing S, Yang Z, Zhu D, Wang S, Bao D, Liang G, Cai Z, Chen H, He W. Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine. Heliyon 2023; 9:e18832. [PMID: 37588610 PMCID: PMC10425907 DOI: 10.1016/j.heliyon.2023.e18832] [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: 01/14/2023] [Revised: 07/13/2023] [Accepted: 07/31/2023] [Indexed: 08/18/2023] Open
Abstract
The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans.
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Affiliation(s)
- Haoxuan Lu
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Li Huang
- Department of Emergency, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Yanqing Xie
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Zhong Zhou
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Hanbin Cui
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Sheng Jing
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Zhuo Yang
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Decai Zhu
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Shiqi Wang
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Donggang Bao
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035, China
| | - Zhennao Cai
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Wenming He
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
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Wang ZY, Guo ZH. Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases. Chin J Integr Med 2023:10.1007/s11655-023-3639-7. [PMID: 37222830 DOI: 10.1007/s11655-023-3639-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2023] [Indexed: 05/25/2023]
Abstract
High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.
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Affiliation(s)
- Zi-Yan Wang
- The First Clinical College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China
| | - Zhi-Hua Guo
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China.
- Hunan Key Laboratory of Colleges and Universities of Intelligent Traditional Chinese Medicine Diagnosis and Preventive Treatment of Chronic Diseases of Hunan Universities of Chinese Medicine, Changsha, 410208, China.
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Fan L, Shi X, Wang Z, Zhang R, Zhang J. Disease identification method based on graph features between pulse cycles. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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The nonlinearity properties of pulse signal of pregnancy in the three trimesters. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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