Validation of a novel traditional chinese medicine pulse diagnostic model using an artificial neural network

Acupuncture in Traditional Chinese Medicine: A Complementary Approach for Cardiovascular Health

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.

A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation

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.

Can Traditional Chinese Medicine Diagnosis Be Parameterized and Standardized? A Narrative Review

The integration of Traditional Chinese Medicine (TCM) in Western health systems and research requires a rational communicable theory, scientific proof of efficacy and safety, and quality control measures. The existence of clear definitions and the diagnosis standardization are critical factors to establish the patient's vegetative functional status accurately and, therefore, systematically apply TCM therapeutics such as the stimulation of reflex skin areas known as acupoints. This science-based conceptualization entails using validated methods, or even developing new systems able to parameterize the diagnosis and assess TCM related effects by objective measurements. Traditionally, tongue and pulse diagnosis and the functional evaluation of action points by pressure sensitivity and physical examination may be regarded as essential diagnostic tools. Parameterizing these techniques is a future key point in the objectification of TCM diagnosis, such as by electronic digital image analysis, mechanical pulse diagnostic systems, or the systematic evaluation of acupoints' electrophysiology. This review aims to demonstrate and critically analyze some achievements and limitations in the clinical application of device-assisted TCM diagnosis systems to evaluate functional physiological patterns. Despite some limitations, tongue, pulse, and electrophysiological diagnosis devices have been reported as a useful tool while establishing a person's functional status.

Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods

Background

Integrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge.

Objective

The purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms.

Methods

A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms.

Results

The Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms.

Conclusions

The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group’s sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods.

Trial Registration

ClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908

Effective attention-based network for syndrome differentiation of AIDS

Background

Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Several previous works were devoted to employing the classical algorithms to classify the syndrome and achieved delightful results. However, the presence of ambiguous symptoms substantially disturbed the performance of syndrome differentiation, This disturbance is always due to the diversity and complexity of the patients’ symptoms.

Methods

To alleviate this issue, we proposed an algorithm based on the multilayer perceptron model with an attention mechanism (ATT-MLP). In particular, we first introduced an attention mechanism to assign different weights for different symptoms among the symptomatic features. In this manner, the symptoms of major significance were highlighted and ambiguous symptoms were restrained. Subsequently, those weighted features were further fed into an MLP to predict the syndrome type of AIDS.

Results

Experimental results for a real-world AIDS dataset show that our framework achieves significant and consistent improvements compared to other methods. Besides, our model can also capture the key symptoms corresponding to each type of syndrome.

Conclusion

In conclusion, our proposed method can learn these intrinsic correlations between symptoms and types of syndromes. Our model is able to learn the core cluster of symptoms for each type of syndrome from limited data, while assisting medical doctors to diagnose patients efficiently.

A machine learning method correlating pulse pressure wave data with pregnancy

Pulse feeling , representing the tactile arterial palpation of the heartbeat, has been widely used in traditional Chinese medicine (TCM) to diagnose various diseases. The quantitative relationship between the pulse wave and health conditions however has not been investigated in modern medicine. In this paper, we explored the correlation between pulse pressure wave (PPW), rather than the pulse key features in TCM, and pregnancy by using deep learning technology. This computational approach shows that the accuracy of pregnancy detection by the PPW is 84% with an area under the curve (AUC) of 91%. Our study is a proof of concept of pulse diagnosis and will also motivate further sophisticated investigations on pulse waves.

Fourier Series Analysis for Novel Spatiotemporal Pulse Waves: Normal, Taut, and Slippery Pulse Images

In this article, a three-dimensional pulse image (3DPI) instead of a one-dimensional temporal pulse wave is studied to elucidate its spatiotemporal characteristics. To check the spatial and temporal properties of 3DPI, adopted is Fourier series, in which a ratio (r) is defined as one amplitude divided by the sum of the first three amplitudes of harmonics. A ratio sequence is constituted from 70 to 90 ratios in a heartbeat with 70–90 3DPIs by sampling. Twenty-four subjects (14 males and 10 females with age of 22.2 ± 3.7 years, 20.4 ± 1.4 BMI, and 112.1 ± 4.7 mmHg systolic blood pressure) are involved in this research. There are significant statistical differences in the groups of the normal, taut, and slippery 3DPIs by the first harmonic ratio average (r1¯) and ratio difference (Δr₁) produced from the ratio sequence. The proposed method of this study gives us a novel viewpoint to clarify the spatiotemporal characteristics of pulse images, which can translate and quantize the pulse feeling in Chinese medicine texts.

Precise Detection of Wrist Pulse Using Digital Speckle Pattern Interferometry

Pulse diagnosis is one of the four diagnostic methods of traditional Chinese medicine. However it suffers from the lack of objective and efficient detection method. We propose a noncontact optical method to detect human wrist pulse, aiming at the precise determination of the temporal and spatial distributions of pulse. The method uses the spatial-carrier digital speckle pattern interferometry (DSPI) to measure the micro/nanoscale skin displacement dynamically. Significant improvements in DSPI measurement have been made to allow the DSPI to detect the comprehensive information of the arterial pulsation at locations of Cun, Guan, and Chi. The experimental results prove that the spatiotemporal distributions of pulse can be obtained by the proposed method. The obtained data can be further used to describe most of the pulse parameters such as rate, rhythm, depth, length, width, and contour.

Traditional Chinese medicine wrist pulse-taking is associated with pulse waveform analysis and hemodynamics in hypertension

BACKGROUND

Pulse wave analysis (PWA) quantifies the phenomenon of pulse waveform propagation in patients with cardiovascular diseases, whereas pulse image analysis (PIA) is a subjective examination in traditional Chinese medicine.

OBJECTIVE

This study evaluated the association of PIA with PWA and hemodynamics in patients with hypertension.

DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS

This observational, cross-sectional study enrolled 45 patients (26 men, (55.2 ± 10.3) years, systolic blood pressure (155 ± 28) mmHg, diastolic blood pressure (93 ± 17) mmHg) for assessment of clinical and laboratorial data.

MAIN OUTCOME MEASURES

Primary outcomes comprised: pattern differentiation based on an automated method; PIA at the radial artery using the 'simultaneous pressing' method for identification of factors such as strength (strong/weak), depth (superficial/deep), and speed (fast/moderate/slow); and PWA at the same artery using a noninvasive system.

RESULTS

Significant multivariate main effects were observed for depth (λ=0.648, F5,29 =3.149, P=0.022, η(2) =0.352), strength (λ=0.608, F5,29 =3.736, P=0.010, η(2) =0.392), and speed (λ=0.535, F5,29 =5.302, P=0.002, η(2) =0.465). General effects comprised high values of PWA and blood pressure for superficial, strong, and fast pulse images. A strong pulse was found for pulse pressure ≥ 62.5 mmHg and systolic blood pressure ≥ 149.5 mmHg, whereas a superficial pulse was found for heart rate ≥ 58.25 beats/min; a fast pulse was found for heart rate ≥ 69.6 beats/min and pulse wave velocity ≥ 9.185 m/s.

CONCLUSION

Associations were explained by LaPlace's law, arterial remodeling in hypertension, alongside the traditional criterion for classifying speed in pulse images. PIA is associated with PWA and hemodynamics in patients with hypertension. Systolic and pulse pressures, heart rate, and pulse wave velocity are quantitative variables that have information to describe the qualitative pulse images such as strength, depth and speed.

Key symptoms selection for two major syndromes diagnosis of Chinese medicine in chronic hepatitis B

OBJECTIVE

To identify key symptoms of two major syndromes in chronic hepatitis B (CHB), which can be the clinical evidence for Chinese medicine (CM) doctors to make decisions.

METHODS

Standardization scales on diagnosis for CHB in CM were designed including physical symptoms, tongue and pulse appearance. The total of 695 CHB cases with dampness-heat (DH) syndrome or Pi (Spleen) deficiency (SD) syndrome were collected for feature selection and modeling, another 275 CHB patients were collected in different locations for validation. Key symptoms were selected based on modified information gain (IG), and 5 classifiers were applied to assist with models training and validation. Classification accuracy and area under receiver operating characteristic curves (AUC) were evaluated.

RESULTS

(1) Thirteen DH syndrome key symptoms and 13 SD syndrome key symptoms were selected from original 125 symptoms; (2) The key symptoms could achieve similar or better diagnostic accuracy than the original total symptoms; (3) In the validation phase, the key symptoms could identify syndromes effectively, especially in DH syndrome, which average prediction accuracy on 5 classifiers could achieve 0.864 with the average AUC 0.772.

CONCLUSION

The selected key symptoms could be simple DH and SD syndromes diagnostic elements applied in clinical directly. (Registration N0.: ChiCTR-DCC-10000759).

Altered Impedance of Ear Acupuncture Point MT2 in Breast Cancer Patients: A Preliminary Observation

Skin impedance at acupuncture points (APs) has been used as a diagnostic aid for more than 50 years. In this study, we have a diagnostic tool (JXT-2008) to measure the skin impedance of ear APs of 30 breast cancer patients and the corresponding skin impedance of ear APs of 30 healthy humans, and then we compared these changes in ear AP impedance in breast cancer patients and healthy individuals.

Association of hypertension with physical factors of wrist pulse waves using a computational approach: a pilot study

BACKGROUND

The objectives of this pilot study were to examine the association between hypertension and physical factors of wrist pulse waves to avoid subjective diagnoses in Traditional Chinese Medicine (TCM) and Traditional Korean Medicine (TKM). An additional objective was to assess the predictive power of individual and combined physical factors in order to identify the degree of agreement between diagnosis accuracies using physical factors and using a sphygmomanometer in the prediction of hypertension.

METHODS

In total, 393 women aged 46 to 73 years participated in this study. Logistic regression (LR) and a naïve Bayes algorithm (NB) were used to assess statistically significant differences and the predictive power of hypertension, and a wrapper-based machine learning method was used to evaluate the predictive power of combinations of physical factors.

RESULTS

In both wrists, L-PPI and R-PPI (maximum pulse amplitudes in the left Gwan and right Gwan) were the factors most strongly associated with hypertension after adjusting for age and body mass index (p = <0.001, odds ratio (OR) = 2.006 on the left and p = <0.001, OR = 2.504 on the right), and the best predictors (NB-AUC = 0.692, LR-AUC = 0.7 on the left and NB-AUC = 0.759, LR-AUC = 0.763 on the right). Analyses of both individual and combined physical factors revealed that the predictive power of the physical factors in the right wrist was higher than for the left wrist. The predictive powers of the combined physical factors were higher than those of the best single predictors in both the left and right wrists.

CONCLUSION

We suggested new physical factors related to the sum of the area on the particular region of pulse waves in both wrists. L-PPI and R-PPI among all variables used in this study were good indicators of hypertension. Our findings support the quantification and objectification of pulse patterns and disease in TCM and TKM for complementary and alternative medicine.

Syndrome differentiation in modern research of traditional Chinese medicine

Syndrome differentiation (Bian Zheng) in traditional Chinese medicine (TCM) is the comprehensive analysis of clinical information gained by the four main diagnostic TCM procedures: observation, listening, questioning, and pulse analysis, and it is used to guide the choice of treatment either by acupuncture and/or TCM herbal formulae, that is, Fufang. TCM syndrome differentiation can be used for further stratification of the patients' conditions with certain disease, identified by orthodox medical diagnosis, which could help the improvement of efficacy of the selected intervention. In modern TCM research it is possible to integrate syndrome differentiation with orthodox medical diagnosis leading to new scientific findings in overall medical diagnosis and treatment. In this review, the focus is to screen published evidence on the role of syndrome differentiation in modern TCM research with particular emphasis on basic and clinical research as well as, pharmacological evaluation of TCM herbal formulary for drug discovery.