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Quantum theory-based physical model of the human body in TCM

Digital Chinese Medicine.2022;5(4):354-359. doi:10.1016/j.dcmed.2022.12.002

In the study, a quantum resonant cavity model based on wave-particle duality was proposed for the explanation of the dynamic processes of essence, vigor, and spirit in the human body in traditional Chinese medicine (TCM). It is assumed that there is a macro human order parameter (wave function), and its dynamics are governed by a macro potential field reflecting influences from heaven, earth, and society, and satisfy the generalized Schrodinger equation. This proposed model was applied in the study to interpret basic concepts of human body in TCM, with an aim to unfold the TCM development in the future.

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A new interpretation of TCM pulse diagnosis based on quantum physical model of the human body

Shuna SONG ; Zhensu SHE

Digital Chinese Medicine.2022;5(4):360-366. doi:10.1016/j.dcmed.2022.12.009

Following the quantum theory-based physical model of the human body, a new interpretation of the traditional Chinese medicine (TCM) principle of “Cunkou reads viscera” is presented. Then, a Gaussian pulse wave model as a solution to the Schrodinger equation is shown to accurately describe 19 different pulse shapes, and to quantitatively capture the degree of Yin-Yang attributes of 13 pulse shapes. Furthermore, the model suggests using pulse depth and strength as leading-order quantity and pulse shape as first-order quantity, to characterize the hierarchical resonance between the human body and the environment. The future pulse informatics will focus on determining an individual’s unique quantum human equilibrium state, and diagnose its health state according to the pulse deviation from its equilibrium state, to truly achieve the high level of TCM: “knowing the normal state and reaching the change”.

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Artificial intelligence and its application for cardiovascular diseases in Chinese medicine

Xiaotong CHEN ; Yeuk-Lan Alice LEUNG ; Jiangang SHEN

Digital Chinese Medicine.2022;5(4):367-376. doi:10.1016/j.dcmed.2022.12.003

Cardiovascular diseases (CVDs) are major disease burdens with high mortality worldwide. Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs. The pathological mechanisms and multiple factors involved in CVDs are complex; thus, traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis prediction. Meanwhile, traditional Chinese medicine (TCM) has been widely used for treating CVDs. TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs. Big data have been generated to investigate the scientific basis of TCM diagnostic methods. TCM formulae contain multiple herbal items. Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability. Recent progress in artificial intelligence (AI) technology has allowed these challenges to be resolved, which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae. Herein, we briefly introduce the basic concept and current progress of AI and machine learning (ML) technology, and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs. Furthermore, we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs. We expect the application of AI and ML technology to promote synergy between western medicine and TCM, which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.

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Data-driven based four examinations in TCM: a survey

Dong SUI ; Lei ZHANG ; Fei YANG

Digital Chinese Medicine.2022;5(4):377-385. doi:10.1016/j.dcmed.2022.12.004

Traditional Chinese medicine (TCM) diagnosis is a unique disease diagnosis method with thousands of years of TCM theory and effective experience. Its thinking mode in the process is different from that of modern medicine, which includes the essence of TCM theory. From the perspective of clinical application, the four diagnostic methods of TCM, including inspection, auscultation and olfaction, inquiry, and palpation, have been widely accepted by TCM practitioners worldwide. With the rise of artificial intelligence (AI) over the past decades, AI based TCM diagnosis has also grown rapidly, marked by the emerging of a large number of data-driven deep learning models. In this paper, our aim is to simply but systematically review the development of the data-driven technologies applied to the four diagnostic approaches, i.e. the four examinations, in TCM, including data sets, digital signal acquisition devices, and learning based computational algorithms, to better analyze the development of AI-based TCM diagnosis, and provide references for new research and its applications in TCM settings in the future.

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Research on knowledge reasoning of TCM based on knowledge graphs

Zhiheng GUO ; Qingping LIU ; Beiji ZOU

Digital Chinese Medicine.2022;5(4):386-393. doi:10.1016/j.dcmed.2022.12.005

With the widespread use of Internet, the amount of data in the field of traditional Chinese medicine (TCM) is growing exponentially. Consequently, there is much attention on the collection of useful knowledge as well as its effective organization and expression. Knowledge graphs have thus emerged, and knowledge reasoning based on this tool has become one of the hot spots of research. This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning, and explores the significance of knowledge reasoning. Secondly, the mainstream knowledge reasoning methods, including knowledge reasoning based on traditional rules, knowledge reasoning based on distributed feature representation, and knowledge reasoning based on neural networks are introduced. Then, using stroke as an example, the knowledge reasoning methods are expounded, the principles and characteristics of commonly used knowledge reasoning methods are summarized, and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out. Finally, we summarize the problems faced in the development of knowledge reasoning in TCM, and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.

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Construction and application of knowledge graph of Treatise on Febrile Diseases

Dongbo LIU ; Changfa WEI ; Shuaishuai XIA ; Junfeng YAN

Digital Chinese Medicine.2022;5(4):394-405. doi:10.1016/j.dcmed.2022.12.006

Objective: To establish the knowledge graph of “disease-syndrome-symptom-method-formula” in Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) for reducing the fuzziness and uncertainty of data, and for laying a foundation for later knowledge reasoning and its application. Methods: Under the guidance of experts in the classical formula of traditional Chinese medicine (TCM), the method of “top-down as the main, bottom-up as the auxiliary” was adopted to carry out knowledge extraction, knowledge fusion, and knowledge storage from the five aspects of the disease, syndrome, symptom, method, and formula for the original text of Treatise on Febrile Diseases, and so the knowledge graph of Treatise on Febrile Diseases was constructed. On this basis, the knowledge structure query and the knowledge relevance query were realized in a visual manner. Results: The knowledge graph of “disease-syndrome-symptom-method-formula” in the Treatise on Febrile Diseases was constructed, containing 6 469 entities and 10 911 relational triples, on which the query of entities and their relationships can be carried out and the query result can be visualized. Conclusion The knowledge graph of Treatise on Febrile Diseases systematically realizes its digitization of the knowledge system, and improves the completeness and accuracy of the knowledge representation, and the connection between “disease-syndrome-symptom-treatment-formula”, which is conducive to the sharing and reuse of knowledge can be obtained in a clear and efficient way.

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MF2ResU-Net: a multi-feature fusion deep learning architecture for retinal blood vessel segmentation

Zhenchao CUI ; Shujie SONG ; Jing QI

Digital Chinese Medicine.2022;5(4):406-418. doi:10.1016/j.dcmed.2022.12.008

Objective: For computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation, a novel multi-level method based on the multi-scale fusion residual neural network (MF2ResU-Net) model is proposed. Methods: To obtain refined features of retinal blood vessels, three cascade connected U-Net networks are employed. To deal with the problem of difference between the parts of encoder and decoder, in MF2ResU-Net, shortcut connections are used to combine the encoder and decoder layers in the blocks. To refine the feature of segmentation, atrous spatial pyramid pooling (ASPP) is embedded to achieve multi-scale features for the final segmentation networks. Results: The MF2ResU-Net was superior to the existing methods on the criteria of sensitivity (Sen), specificity (Spe), accuracy (ACC), and area under curve (AUC), the values of which are 0.8013 and 0.8102, 0.9842 and 0.9809, 0.9700 and 0.9776, and 0.9797 and 0.9837, respectively for DRIVE and CHASE DB1. The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels. Conclusion Based on residual connections and multi-feature fusion, the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation features, which can provide another diagnosis method for computer-aided Chinese medical diagnosis.

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Heterogeneous graph construction and node representation learning method of Treatise on Febrile Diseases based on graph convolutional network

Junfeng YAN ; Zhihua WEN ; Beiji ZOU

Digital Chinese Medicine.2022;5(4):419-428. doi:10.1016/j.dcmed.2022.12.007

Objective: To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) dataset and explore an optimal learning method represented with node attributes based on graph convolutional network (GCN). Methods: Clauses that contain symptoms, formulas, and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs, which were used to propose a node representation learning method based on GCN − the Traditional Chinese Medicine Graph Convolution Network (TCM-GCN). The symptom-formula, symptom-herb, and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes, and thus acquiring the nodes’ sum-aggregations of symptoms, formulas, and herbs to lay a foundation for the downstream tasks of the prediction models. Results: Comparisons among the node representations with multi-hot encoding, non-fusion encoding, and fusion encoding showed that the Precision@10, Recall@10, and F1-score@10 of the fusion encoding were 9.77%, 6.65%, and 8.30%, respectively, higher than those of the non-fusion encoding in the prediction studies of the model. Conclusion Node representations by fusion encoding achieved comparatively ideal results, indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.

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Research status and prospect of tongue image diagnosis analysis based on machine learning

Jiatuo XU ; Tao JIANG ; Shi LIU

Digital Chinese Medicine.2024;7(1):3-12. doi:10.1016/j.dcmed.2024.04.002

Image-based intelligent diagnosis represents a trending research direction in the field of tongue diagnosis in traditional Chinese medicine(TCM).In recent years,machine learning techniques,including convolutional neural networks(CNNs)and Transformers,have been widely used in the analysis of medical images,such as computed tomography(CT)and nucle-ar magnetic resonance imaging(MRI).These techniques have significantly enhanced the effi-ciency and accuracy of decision-making in TCM practices.Advanced artificial intelligence(AI)technologies have also provided new opportunities for the research and development of medical equipment and TCM tongue diagnosis,resulting in improved standardization and in-telligence of the tongue diagnostic procedures.Although traditional image analysis methods could transform tongue images into scientific and analyzable data,recognizing and analyzing images that capture complicated tongue features such as tooth-marked tongue,tongue spots and prickles,fissured tongue,variations in coating thickness,tongue texture(curdy and greasy),and tongue presence(peeled coating)continues posing significant challenges in con-temporary tongue diagnosis research.Therefore,the employment of machine learning tech-niques in the analysis of tongue shape and texture features as well as their applications in TCM diagnosis is the focus of this study.In the study,both traditional and deep learning im-age analysis techniques were summarized and analyzed to figure out their value in predicting disease risks by observing the tongue shapes and textures,aiming to open a new chapter for the development and application of AI in TCM tongue diagnosis research.In short,the com-bination of TCM tongue diagnosis and AI technologies,will not only enhance the scientific basis of tongue diagnosis but also improve its clinical applicability,thereby advancing the modernization of TCM diagnostic and therapeutic practices.

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Distribution of traditional Chinese medicine pattern types and prognostic risk factors in patients undergoing percutaneous coronary intervention(PCI):a systematic review and meta-analysis

Jieyun LI ; Leixin HONG ; Jiekee LIN ; Yumo XIA ; Xin'ang XIAO ; Zhaoxia XU

Digital Chinese Medicine.2024;7(1):13-28. doi:10.1016/j.dcmed.2024.04.003

Objective To clarify the distribution of traditional Chinese medicine(TCM)pattern and its associated risk factors after percutaneous coronary intervention(PCI),and evaluate the re-porting quality of existing studies to guide future research standardization. Methods English databases including PubMed,Cochrane Library,and Web of Science,as well as Chinese databases including China National Knowledge Infrastructure(CNKI),China Scientific Journal Database(VIP),and Wanfang Database were searched to retrieve papers about PCI.The time span for the paper retrieval was set from the foundation of the databases to October 1,2023.Statistical analyses were performed using Stata 12 and Python(V 3.9).The Strengthening the Reporting of Observational Studies in Epidemiology(STROBE)statement was used to assess the reporting quality of included studies. Results Overall,1 356 articles were selected,and 40 cross-sectional studies were included with 10 270 participants.The most common TCM patterns before,one to two weeks after,and six months to one year after PCI was Qi stagnation and blood stasis(n=261,36.45%),inter-twined phlegm and blood stasis(n=109,27.18%),and Qi deficiency and blood stasis(n=645,37.03%),respectively.Smoking[odds ratio(OR)=1.15,95%confidence interval(CI)(0.83-1.47),I2=24.7%,P=0.257],pattern of congealing cold and Qi stagnation[OR=4.62,95%CI(1.37-7.86),I2=61.6%,P=0.074],and low-density lipoprotein(LDL)[OR=1.38,95%CI(0.92-1.85),I2=12.2%,P=0.286]were risk factors for restenosis.Hypertension[OR=7.26,95%CI(3.54-14.88),I2=91.6%,P=0.001],and overweight[i.e.,body mass index(BMI)>23][OR=1.20,95%CI(1.07-1.35),I2=85.3%,P=0.009]were significant risk factors of concomi-tant anxiety. Conclusion This systematic review and meta-analysis revealed that patients with different TCM pattern types have distinct characteristics and risk factors after PCI.More high-quality studies are warranted to provide supportive evidence for future research and clinical practice.

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