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Yang J, Wang H, Liu P, Lu Y, Yao M, Yan H. Prediction of hypertension risk based on multiple feature fusion. J Biomed Inform 2024; 157:104701. [PMID: 39047932 DOI: 10.1016/j.jbi.2024.104701] [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: 03/29/2024] [Revised: 07/12/2024] [Accepted: 07/20/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE In the application of machine learning to the prediction of hypertension, many factors have seriously affected the classification accuracy and generalization performance. We propose a pulse wave classification model based on multi-feature fusion for accuracy prediction of hypertension. METHODS AND MATERIALS We propose an ensemble under-sampling model with dynamic weights to decrease the influence of class imbalance on classification, further to automatically classify of hypertension on inquiry diagnosis. We also build a deep learning model based on hybrid attention mechanism, which transforms pulse waves to feature maps for extraction of in-depth features, so as to automatically classify hypertension on pulse diagnosis. We build the multi-feature fusion model based on dynamic Dempster/Shafer (DS) theory combining inquiry diagnosis and pulse diagnosis to enhance fault tolerance of prediction for multiple classifiers. In addition, this study calculates feature importance ranking of scale features on inquiry diagnosis and temporal and frequency-domain features on pulse diagnosis. RESULTS The accuracy, sensitivity, specificity, F1-score and G-mean after 5-fold cross-validation were 94.08%, 93.43%, 96.86%, 93.45% and 95.12%, respectively, based on the hypertensive samples of 409 cases from Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We find the key factors influencing hypertensive classification accuracy, so as to assist in the prevention and clinical diagnosis of hypertension. CONCLUSION Compared with the state-of-the-art models, the multi-feature fusion model effectively utilizes the patients' correlated multimodal features, and has higher classification accuracy and generalization performance.
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Affiliation(s)
- Jingdong Yang
- Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Han Wang
- Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Peng Liu
- Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yuhang Lu
- Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Minghui Yao
- Department of Traditional Chinese Medicine Diagnosis, Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Haixia Yan
- Department of Traditional Chinese Medicine Diagnosis, Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
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Bae H, Park SY, Kim CE. A practical guide to implementing artificial intelligence in traditional East Asian medicine research. Integr Med Res 2024; 13:101067. [PMID: 39253696 PMCID: PMC11381867 DOI: 10.1016/j.imr.2024.101067] [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: 05/14/2024] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 09/11/2024] Open
Abstract
In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.
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Affiliation(s)
- Hyojin Bae
- Department of Physiology, Seoul National University College of Medicine, Seoul, Korea
| | - Sa-Yoon Park
- Department of Physiology, College of Korean Medicine, Wonkwang University, Iksan, Korea
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea
| | - Chang-Eop Kim
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea
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Wang M, Yin F, Kong L, Yang L, Sun H, Sun Y, Yan G, Han Y, Wang X. Chinmedomics: a potent tool for the evaluation of traditional Chinese medicine efficacy and identification of its active components. Chin Med 2024; 19:47. [PMID: 38481256 PMCID: PMC10935806 DOI: 10.1186/s13020-024-00917-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
As an important part of medical science, Traditional Chinese Medicine (TCM) attracts much public attention due to its multi-target and multi-pathway characteristics in treating diseases. However, the limitations of traditional research methods pose a dilemma for the evaluation of clinical efficacy, the discovery of active ingredients and the elucidation of the mechanism of action. Therefore, innovative approaches that are in line with the characteristics of TCM theory and clinical practice are urgently needed. Chinmendomics, a newly emerging strategy for evaluating the efficacy of TCM, is proposed. This strategy combines systems biology, serum pharmacochemistry of TCM and bioinformatics to evaluate the efficacy of TCM with a holistic view by accurately identifying syndrome biomarkers and monitoring their complex metabolic processes intervened by TCM, and finding the agents associated with the metabolic course of pharmacodynamic biomarkers by constructing a bioinformatics-based correlation network model to further reveal the interaction between agents and pharmacodynamic targets. In this article, we review the recent progress of Chinmedomics to promote its application in the modernisation and internationalisation of TCM.
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Affiliation(s)
- Mengmeng Wang
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Fengting Yin
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Ling Kong
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Le Yang
- State Key Laboratory of Dampness Syndrome, The Second Affiliated Hospital Guangzhou University of Chinese Medicine, Dade Road 111, Guangzhou, China
| | - Hui Sun
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China.
| | - Ye Sun
- State Key Laboratory of Dampness Syndrome, The Second Affiliated Hospital Guangzhou University of Chinese Medicine, Dade Road 111, Guangzhou, China
| | - Guangli Yan
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Ying Han
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Xijun Wang
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China.
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
- State Key Laboratory of Dampness Syndrome, The Second Affiliated Hospital Guangzhou University of Chinese Medicine, Dade Road 111, Guangzhou, China.
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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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Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol 2024; 15:1181183. [PMID: 38464717 PMCID: PMC10921893 DOI: 10.3389/fphar.2024.1181183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024] Open
Abstract
Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.
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Affiliation(s)
- E. Zhou
- Yuhu District Healthcare Security Administration, Xiangtan, China
| | - Qin Shen
- Department of Respiratory Medicine, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yang Hou
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
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Zhang N, Jiang Z, Li M, Zhang D. A novel multi-feature learning model for disease diagnosis using face skin images. Comput Biol Med 2024; 168:107837. [PMID: 38086142 DOI: 10.1016/j.compbiomed.2023.107837] [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: 04/06/2023] [Revised: 11/15/2023] [Accepted: 12/07/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Facial skin characteristics can provide valuable information about a patient's underlying health conditions. OBJECTIVE In practice, there are often samples with divergent characteristics (commonly known as divergent samples) that can be attributed to environmental factors, living conditions, or genetic elements. These divergent samples significantly degrade the accuracy of diagnoses. METHODOLOGY To tackle this problem, we propose a novel multi-feature learning method called Multi-Feature Learning with Centroid Matrix (MFLCM), which aims to mitigate the influence of divergent samples on the accurate classification of samples located on the boundary. In this approach, we introduce a novel discriminator that incorporates a centroid matrix strategy and simultaneously adapt it to a classifier in a unified model. We effectively apply the centroid matrix to the embedding feature spaces, which are transformed from the multi-feature observation space, by calculating a relaxed Hamming distance. The purpose of the centroid vectors for each category is to act as anchors, ensuring that samples from the same class are positioned close to their corresponding centroid vector while being pushed further away from the remaining centroids. RESULTS Validation of the proposed method with clinical facial skin dataset showed that the proposed method achieved F1 scores of 92.59%, 83.35%, 82.84% and 85.46%, respectively for the detection the Healthy, Diabetes Mellitus (DM), Fatty Liver (FL) and Chronic Renal Failure (CRF). CONCLUSION Experimental results demonstrate the superiority of the proposed method compared with typical classifiers single-view-based and state-of-the-art multi-feature approaches. To the best of our knowledge, this study represents the first to demonstrate concept of multi-feature learning using only facial skin images as an effective non-invasive approach for simultaneously identifying DM, FL and CRF in Han Chinese, the largest ethnic group in the world.
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Affiliation(s)
- Nannan Zhang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
| | - Zhixing Jiang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
| | - Mu Li
- Harbin Institute of Technology at Shenzhen, Shenzhen, China.
| | - David Zhang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
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Wang X, Xie Y, Yang X, Gu D. Internet-Based Healthcare Knowledge Service for Improvement of Chinese Medicine Healthcare Service Quality. Healthcare (Basel) 2023; 11:2170. [PMID: 37570410 PMCID: PMC10418357 DOI: 10.3390/healthcare11152170] [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: 05/30/2023] [Revised: 07/21/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
With the development of new-generation information technology and increasing health needs, the requirements for Chinese medicine (CM) services have shifted toward the 5P medical mode, which emphasizes preventive, predictive, personalized, participatory, and precision medicine. This implies that CM knowledge services need to be smarter and more sophisticated. This study adopted a bibliometric approach to investigate the current state of development of CM knowledge services, and points out that accurate knowledge service is an inevitable requirement for the modernization of CM. We summarized the concept of smart CM knowledge services and highlighted its main features, including medical homogeneity, knowledge service intelligence, integration of education and research, and precision medicine. Additionally, we explored the intelligent service method of traditional Chinese medicine under the 5P medical mode to support CM automatic knowledge organization and safe sharing, human-machine collaborative knowledge discovery and personalized dynamic knowledge recommendation. Finally, we summarized the innovative modes of CM knowledge services. Our research will guide the quality assurance and innovative development of the traditional Chinese medicine knowledge service model in the era of digital intelligence.
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Affiliation(s)
- Xiaoyu Wang
- The Department of Pharmacy, Anhui University of Traditional Chinese Medicine, Hefei 230031, China;
| | - Yi Xie
- The School of Management, Hefei University of Technology, Hefei 230009, China; (X.Y.); (D.G.)
| | - Xuejie Yang
- The School of Management, Hefei University of Technology, Hefei 230009, China; (X.Y.); (D.G.)
| | - Dongxiao Gu
- The School of Management, Hefei University of Technology, Hefei 230009, China; (X.Y.); (D.G.)
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Segawa M, Iizuka N, Ogihara H, Tanaka K, Nakae H, Usuku K, Yamaguchi K, Wada K, Uchizono A, Nakamura Y, Nishida Y, Ueda T, Shiota A, Hasunuma N, Nakahara K, Hebiguchi M, Hamamoto Y. Objective evaluation of tongue diagnosis ability using a tongue diagnosis e-learning/e-assessment system based on a standardized tongue image database. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 5:1050909. [PMID: 36993786 PMCID: PMC10040798 DOI: 10.3389/fmedt.2023.1050909] [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: 09/22/2022] [Accepted: 02/10/2023] [Indexed: 03/14/2023] Open
Abstract
Background In Kampo medicine, tongue examination is used to diagnose the pathological condition "Sho," but an objective evaluation method for its diagnostic ability has not been established. We constructed a tongue diagnosis electronic learning and evaluation system based on a standardized tongue image database. Purpose This study aims to verify the practicality of this assessment system by evaluating the tongue diagnosis ability of Kampo specialists (KSs), medical professionals, and students. Methods In the first study, we analyzed the answer data of 15 KSs in an 80-question tongue diagnosis test that assesses eight aspects of tongue findings and evaluated the (i) test score, (ii) test difficulty and discrimination index, (iii) diagnostic consistency, and (iv) diagnostic match rate between KSs. In the second study, we administered a 20-question common Kampo test and analyzed the answer data of 107 medical professionals and 56 students that assessed the tongue color discrimination ability and evaluated the (v) correct answer rate, (vi) test difficulty, and (vii) factors related to the correct answer rate. Result In the first study, the average test score was 62.2 ± 10.7 points. Twenty-eight questions were difficult (correct answer rate, <50%), 34 were moderate (50%-85%), and 18 were easy (≥85%). Regarding intrarater reliability, the average diagnostic match rate of five KSs involved in database construction was 0.66 ± 0.08, and as for interrater reliability, the diagnostic match rate between the 15 KSs was 0.52 (95% confidence interval, 0.38-0.65) for Gwet's agreement coefficient 1, and the degree of the match rate was moderate. In the second study, the difficulty level of questions was moderate, with a correct rate of 81.3% for medical professionals and 82.1% for students. The discrimination index was good for medical professionals (0.35) and poor for students (0.06). Among medical professionals, the correct answer group of this question had a significantly higher total score on the Kampo common test than the incorrect answer group (85.3 ± 8.4 points vs. 75.8 ± 11.8 points, p < 0.01). Conclusion This system can objectively evaluate tongue diagnosis ability and has high practicality. Utilizing this system can be expected to contribute to improving learners' tongue diagnosis ability and standardization of tongue diagnosis.
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Affiliation(s)
- Makoto Segawa
- Department of Kampo Medicine, Yamaguchi University Hospital, Ube, Japan
| | - Norio Iizuka
- Department of Kampo Medicine, Yamaguchi University Hospital, Ube, Japan
- Yamaguchi Health Examination Center, Ogori-shimogo, Japan
| | - Hiroyuki Ogihara
- Department of Computer Science and Electronic Engineering, National Institute of Technology, Tokuyama Collage, Shunan, Japan
| | - Koichiro Tanaka
- Department of Traditional Medicine, Faculty of Medicine, Toho University, Tokyo, Japan
| | - Hajime Nakae
- Department of Emergency and Critical Care Medicine, Akita University Graduate School of Medicine, Akita, Japan
| | | | - Kojiro Yamaguchi
- Outpatient of Dental Chronic Disease, TANAKA Orthodontic Clinic, Medical Corporation HAYANOKAI, Kagoshima, Japan
| | - Kentaro Wada
- Division of Nephrology and Dialysis, Department of Internal Medicine, Nippon Kokan Fukuyama Hospital, Hiroshima, Japan
| | | | | | - Yoshihiro Nishida
- Department of Obstetrics and Gynecology, Faculty of Medicine, Oita University, Oita, Japan
| | | | - Atsuko Shiota
- Department of Health Sciences, Faculty of Medicine, Kagawa University, Kitagun, Japan
| | - Naoko Hasunuma
- Department of Medical Education, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | | | | | - Yoshihiko Hamamoto
- Division of Electrical, Electronic and Information Engineering, Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Japan
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Park MS, Kim J, Kim KH, Yoo HR, Chae I, Lee J, Joo IH, Kim DH. Modern concepts and biomarkers of blood stasis in cardio- and cerebrovascular diseases from the perspectives of Eastern and Western medicine: a scoping review protocol. JBI Evid Synth 2023; 21:214-222. [PMID: 35946908 DOI: 10.11124/jbies-22-00020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The objective of this review is to provide a modern definition and identify potential biomarkers of blood stasis in cardio- and cerebrovascular diseases by mapping, comparing, and combining Eastern and Western concepts. INTRODUCTION Blood stasis is a pathological concept found in both Eastern and Western medical literature. In traditional East Asian medicine, blood stasis is a differential syndrome characterized by stagnant blood flow in various parts of the body. Similarly, in Western medicine, various diseases, especially cardio- and cerebrovascular diseases, are known to be accompanied by blood stasis. Numerous scientific studies on blood stasis have been conducted over the last decade, and there is a need to synthesize those results. INCLUSION CRITERIA We will use the keywords "blood stasis," "blood stagnation," "blood stagnant," and "blood congestion" in 3 electronic databases: PubMed, Cochrane CENTRAL, and Google Scholar. In addition, we will use the keywords "어혈" and "혈어" in 4 Korean electronic databases (ie, NDSL, OASIS, KISS, and DBpia). Peer-reviewed articles published from 2010 to the present that focus on blood stasis in cardio- and cerebrovascular diseases in human subjects according to the International Classification of Diseases 11 th revision categories BA00-BE2Z, 8B00-8B2Z, 8E64, and 8E65 will be included. Reviews, opinion articles, in vivo, in vitro, and in silico preclinical studies will be excluded. METHODS We will follow the frameworks by Arksey and O'Malley and Levac et al. as well as JBI guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews. Two reviewers will independently search and screen titles and abstracts followed by full-text screening of eligible studies. If there are discrepancies between the 2 reviewers, a third reviewer will be consulted to make the final decision. We will use descriptive narrative, tabular, and graphical displays, and content analysis to present the results. SCOPING REVIEW REGISTRATION Open Science Framework https://osf.io/gv4ym.
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Affiliation(s)
- Miso S Park
- Clinical Trial Center, Daejeon Korean Medicine Hospital of Daejeon University, Daejeon, Republic of Korea.,Department of Cardiology and Neurology of Korean Medicine, College of Korean Medicine, Daejeon University, Daejeon, Republic of Korea
| | - Jihye Kim
- Digital Clinical Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Keun Ho Kim
- Digital Clinical Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Ho-Ryong Yoo
- Clinical Trial Center, Daejeon Korean Medicine Hospital of Daejeon University, Daejeon, Republic of Korea.,Department of Cardiology and Neurology of Korean Medicine, College of Korean Medicine, Daejeon University, Daejeon, Republic of Korea
| | - Incheol Chae
- Department of Cardiology and Neurology of Korean Medicine, College of Korean Medicine, Daejeon University, Daejeon, Republic of Korea
| | - Juho Lee
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology (KRICT), University of Science and Technology (UST), Republic of Korea
| | - In Hwan Joo
- Department of Pathology, Daejeon University College of Korean Medicine, Daejeon, Republic of Korea
| | - Dong Hee Kim
- Department of Pathology, Daejeon University College of Korean Medicine, Daejeon, Republic of Korea
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Zhang Q, Wen J, Zhou J, Zhang B. Missing-view completion for fatty liver disease detection. Comput Biol Med 2022; 150:106097. [PMID: 36244304 DOI: 10.1016/j.compbiomed.2022.106097] [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: 04/21/2022] [Revised: 08/22/2022] [Accepted: 09/10/2022] [Indexed: 11/15/2022]
Abstract
Fatty liver disease is a common disease that causes extra fat storage in an individual's liver. Patients with fatty liver disease may progress to cirrhosis and liver failure, further leading to liver cancer. The prevalence of fatty liver disease ranges from 10% to 30% in many countries. In general, detecting fatty liver requires professional neuroimaging modalities or methods such as computed tomography, ultrasound, and medical experts' practical experiences. Considering this point, finding intelligent electronic noninvasive diagnostic approaches are desired at present. Currently, most existing works in the area of computerized noninvasive disease detection often apply one view (modality) or perform multi-view (several modalities) analysis, e.g., face, tongue, and/or sublingual for disease detection. The multi-view data of patients provides more complementary information for diagnosis. However, due to the conditions of data acquisition, interference by human factors, etc., many multi-view data are defective with some missing-view information, making these multi-view data difficult to evaluate. This factor largely affects the performance of classifying disease and the development of fully computerized noninvasive methods. Thus, the purpose of this study is to address the missing view issue among noninvasive disease detection. In this work, a multi-view dataset containing facial, sublingual vein, and tongue images are initially processed to produce corresponding feature for incomplete multi-view disease diagnostic evaluation. Hereby, we propose a novel method, i.e., multi-view completion, to process the incomplete multi-view data in order to complete the missing-view information for classifying fatty liver disease from healthy candidates. In particular, this method can explore the intra-view and inter-view information to produce the missing-view data effectively. Extensive experiments on a collected dataset with 220 fatty liver patients and 220 healthy samples show that our proposed approach achieves better diagnostic results with missing-view completion compared to the original incomplete multi-view data under various classifiers. Related results prove that our method can effectively process the missing-view issue and improve the noninvasive disease detection performance.
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Affiliation(s)
- Qi Zhang
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Macau, China
| | - Jie Wen
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Jianhang Zhou
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Macau, China
| | - Bob Zhang
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Macau, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, China.
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11
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A prediction model of qi stagnation: A prospective observational study referring to two existing models. Comput Biol Med 2022; 146:105619. [DOI: 10.1016/j.compbiomed.2022.105619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/10/2022] [Accepted: 05/12/2022] [Indexed: 11/22/2022]
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Chen D, Li Y. PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features. Front Genet 2022; 13:875112. [PMID: 35547252 PMCID: PMC9081368 DOI: 10.3389/fgene.2022.875112] [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: 02/13/2022] [Accepted: 03/07/2022] [Indexed: 12/03/2022] Open
Abstract
The major histocompatibility complex (MHC) is a large locus on vertebrate DNA that contains a tightly linked set of polymorphic genes encoding cell surface proteins essential for the adaptive immune system. The groups of proteins encoded in the MHC play an important role in the adaptive immune system. Therefore, the accurate identification of the MHC is necessary to understand its role in the adaptive immune system. An effective predictor called PredMHC is established in this study to identify the MHC from protein sequences. Firstly, PredMHC encoded a protein sequence with mixed features including 188D, APAAC, KSCTriad, CKSAAGP, and PAAC. Secondly, three classifiers including SGD, SMO, and random forest were trained on the mixed features of the protein sequence. Finally, the prediction result was obtained by the voting of the three classifiers. The experimental results of the 10-fold cross-validation test in the training dataset showed that PredMHC can obtain 91.69% accuracy. Experimental results on comparison with other features, classifiers, and existing methods showed the effectiveness of PredMHC in predicting the MHC.
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Affiliation(s)
- Dong Chen
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Yanjuan Li
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
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13
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Guo C, Jiang Z, He H, Liao Y, Zhang D. Wrist pulse signal acquisition and analysis for disease diagnosis: A review. Comput Biol Med 2022; 143:105312. [PMID: 35203039 DOI: 10.1016/j.compbiomed.2022.105312] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 01/22/2022] [Accepted: 02/07/2022] [Indexed: 11/26/2022]
Abstract
Pulse diagnosis (PD) plays an indispensable role in healthcare in China, India, Korea, and other Orient countries. It requires considerable training and experience to master. The results of pulse diagnosis rely heavily on the practitioner's subjective analysis, which means that the results from different physicians may be inconsistent. To overcome these drawbacks, computational pulse diagnosis (CPD) is used with advanced sensing techniques and analytical methods. Focusing on the main processes of CPD, this paper provides a systematic review of the latest advances in pulse signal acquisition, signal preprocessing, feature extraction, and signal recognition. The most relevant principles and applications are presented along with current progress. Extensive comparisons and analyses are conducted to evaluate the merits of different methods employed in CPD. While much progress has been made, a lack of datasets and benchmarks has limited the development of CPD. To address this gap and facilitate further research, we present a benchmark to evaluate different methods. We conclude with observations of the status and prospects of CPD.
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Affiliation(s)
- Chaoxun Guo
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Research Institute of Big Data, Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
| | - Zhixing Jiang
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
| | - Haoze He
- New York University, New York, 10012, New York, United States
| | - Yining Liao
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China
| | - David Zhang
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Research Institute of Big Data, Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
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Segawa M, Iizuka N, Ogihara H, Tanaka K, Nakae H, Usuku K, Hamamoto Y. Construction of a Standardized Tongue Image Database for Diagnostic Education: Development of a Tongue Diagnosis e-Learning System. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 3:760542. [PMID: 35047962 PMCID: PMC8757883 DOI: 10.3389/fmedt.2021.760542] [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: 10/28/2021] [Accepted: 11/26/2021] [Indexed: 11/13/2022] Open
Abstract
Tongue examination is an important diagnostic method for judging pathological conditions in Kampo (traditional Japanese medicine), but it is not easy for beginners to learn the diagnostic technique. One reason is that there are few objective diagnostic criteria for tongue examination findings, and the educational method for tongue examination is not standardized in Japan, warranting the need for a tongue image database for e-learning systems that could dramatically improve the efficiency of education. Therefore, we constructed a database comprising tongue images whose findings were determined on the basis of votes given by five Kampo medicine specialists (KMSs) and confirmed the educational usefulness of the database for tongue diagnosis e-learning systems. The study was conducted in the following five steps: development of a tongue imaging collection system, collection of tongue images, evaluation and annotation of tongue images, development of a tongue diagnosis e-learning system, and verification of the educational usefulness of this system. Five KMSs evaluated the tongue images obtained from 125 participants in the following eight aspects: (i) tongue body size, (ii) tongue body color, (iii) tongue body dryness and wetness, (iv) tooth marks on the edge of the tongue, (v) cracks on the surface of the tongue, (vi) thickness of tongue coating, (vii) color of tongue coating, and (viii) dryness and wetness of tongue coating. Medical students (MSs) were given a tongue diagnosis test using an e-learning system after a lecture on tongue diagnosis. The cumulative and individual match rates (%) (individual match rates of 100% (5/5), 80% (4/5), and 60% (3/5) are shown in parentheses, respectively) were as follows: (i) tongue body size: 92.8 (26.4/26.4/40.0); (ii) tongue body color: 83.2 (10.4/20.8/52.0); (iii) tongue body dryness and wetness: 88.8 (13.6/34.4/40.8); (iv) tooth marks on the edge of the tongue: 88.8 (6.4/35.2/47.2); (v) cracks on the surface of the tongue: 96.8 (24.0/35.2/37.6); (vi) thickness of tongue coating: 84.8 (7.2/21.6/56.0); (vii) color of tongue coating: 88.0 (15.2/37.6/35.2); and (viii) dryness and wetness of tongue coating: 74.4 (4.8/19.2/50.4). The test showed that the tongue diagnosis ability of MSs who attended a lecture on tongue diagnosis was almost the same as that of KMSs. We successfully constructed a tongue image database standardized for training specialists on tongue diagnosis and confirmed the educational usefulness of the e-learning system using a database. This database will contribute to the standardization and popularization of Kampo education.
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Affiliation(s)
- Makoto Segawa
- Department of Kampo Medicine, Yamaguchi University Hospital, Ube, Japan
| | - Norio Iizuka
- Department of Kampo Medicine, Yamaguchi University Hospital, Ube, Japan.,Yamaguchi Health Examination Center, Yamaguchi, Japan
| | - Hiroyuki Ogihara
- Division of Electrical, Electronic and Information Engineering, Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Japan
| | - Koichiro Tanaka
- Department of Traditional Medicine, Faculty of Medicine, Toho University, Tokyo, Japan
| | - Hajime Nakae
- Department of Emergency and Critical Care Medicine, Akita University Graduate School of Medicine, Akita, Japan
| | - Koichiro Usuku
- Department of Medical Information Science and Administrative Planning, Kumamoto University Hospital, Kumamoto, Japan
| | - Yoshihiko Hamamoto
- Division of Electrical, Electronic and Information Engineering, Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Japan
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Xie J, Jing C, Zhang Z, Xu J, Duan Y, Xu D. Digital tongue image analyses for health assessment. MEDICAL REVIEW (BERLIN, GERMANY) 2021; 1:172-198. [PMID: 37724302 PMCID: PMC10388765 DOI: 10.1515/mr-2021-0018] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 12/13/2021] [Indexed: 09/20/2023]
Abstract
Traditional Chinese Medicine (TCM), as an effective alternative medicine, utilizes tongue diagnosis as a major method to assess the patient's health status by examining the tongue's color, shape, and texture. Tongue images can also give the pre-disease indications without any significant disease symptoms, which provides a basis for preventive medicine and lifestyle adjustment. However, traditional tongue diagnosis has limitations, as the process may be subjective and inconsistent. Hence, computer-aided tongue diagnoses have a great potential to provide more consistent and objective health assessments. This paper reviewed the current trends in TCM tongue diagnosis, including tongue image acquisition hardware, tongue segmentation, feature extraction, color correction, tongue classification, and tongue diagnosis system. We also present a case of TCM constitution classification based on tongue images.
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Affiliation(s)
- Jiacheng Xie
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Congcong Jing
- School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziyang Zhang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Jiatuo Xu
- School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ye Duan
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
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Zhou J, Zhang Q, Zhang B. Two-phase non-invasive multi-disease detection via sublingual region. Comput Biol Med 2021; 137:104782. [PMID: 34520987 DOI: 10.1016/j.compbiomed.2021.104782] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022]
Abstract
Non-invasive multi-disease detection is an active technology that detects human diseases automatically. By observing images of the human body, computers can make inferences on disease detection based on artificial intelligence and computer vision techniques. The sublingual vein, lying on the lower part of the human tongue, is a critical identifier in non-invasive multi-disease detection, reflecting health status. However, few studies have fully investigated non-invasive multi-disease detection via the sublingual vein using a quantitative method. In this paper, a two-phase sublingual-based disease detection framework for non-invasive multi-disease detection was proposed. In this framework, sublingual vein region segmentation was performed on each image in the first phase to achieve the region with the highest probability of covering the sublingual vein. In the second phase, features in this region were extracted, and multi-class classification was applied to these features to output a detection result. To better represent the characterisation of the obtained sublingual vein region, multi-feature representations were generated of the sublingual vein region (based on color, texture, shape, and latent representation). The effectiveness of sublingual-based multi-disease detection was quantitatively evaluated, and the proposed framework was based on 1103 sublingual vein images from patients in different health status categories. The best multi-feature representation was generated based on color, texture, and latent representation features with the highest accuracy of 98.05%.
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Affiliation(s)
- Jianhang Zhou
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Taipa, Macau, China; Shenzhen Research Institute of Big Data, Shenzhen, 518172, China.
| | - Qi Zhang
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Taipa, Macau, China.
| | - Bob Zhang
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Taipa, Macau, China.
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