Evaluation of reducing the south and reinforcing the north method on postmenopausal atherosclerosis disease based on 4 diagnostic objectifications: A prospective observational study.

Reducing the south and reinforcing the north method (RSRN) has a positive effect on atherosclerosis disease. However, there is a lack of objective standards based on the quantification of 4 diagnostic methods in evaluating the improvement or effectiveness of the treatment. This study aimed to explore the quantitative evaluation of the therapeutic effect of RSRN on postmenopausal atherosclerosis based on the 4 diagnostic methods. The observational prospective cohort study was conducted at Longhua hospital Shanghai University of traditional Chinese medicine. According to the inclusion criteria, 96 patients (disease group) and 38 healthy cases (control group) were selected, the pulse parameters were compared between the 2 groups to demonstrate the reliability and success of the disease model. Then 4 diagnostic information before and after RSRN treatment were collected and statistical analyzed by 1-way analysis of variance (ANOVA) (with Bonferroni correction). Furthermore, social network analysis was used to analyze the changes of symptoms, tongue, pulse, and complexion characteristics before and after treatment. There was a significant difference in pulse parameters between the disease group and the control group. The pulse parameters t1, h3, h3/h1, h4/h1, S, As, and w values in disease group were higher than those in control group, while the h5, h5/h1, and Ad values were lower than those in control group (P < .05). After the treatment of RSRN, the clinical symptoms of patients were greatly improved. The facial color indexes L, a, b values of the disease group at week 6 were different from those at week 0 (P < .05). The overall brightness and chroma of the patient's facial color were significantly improved. The patients had virtual string pulse at week 0, and mainly string I and string II at week 7. The pulse parameters t1, t5, w, w/t, h1, h5, h3/h1, and h5/h1 values at week 7 were different from those at weeks 0, 1, 2 (P < .05); the tongue image was mainly red and crimson, peeling or greasy fur at week 0, while at weeks 6, 7, mainly light red, or thin white tongue. The RSRN method can regulate the complexion, tongue and pulse condition, clinical symptoms of postmenopausal atherosclerosis.

Objective Evaluation of Pulse Width Using an Array Pulse Diagram.

BACKGROUND: Pulse width, which can reflect qi, blood excess, and deficiency, has been used for diagnosing diseases and determining the prognosis in traditional Chinese medicine (TCM). This study aimed to devise an objective method to measure the pulse width based on an array pulse diagram for objective diagnosis. METHODS: The channel 6, the region wherein the pulse wave signal is the strongest, is located in the middle of the pulse sensor array and at the guan position of cunkou during data collection. Therefore, the main wave (h1) time of the pulse wave was collected from the channel 6 through calculation. The left h1 time was collected from the remaining 11 channels. The amplitudes at these time points were extracted as the h1 amplitudes for each channel. However, the pulse width could not be calculated accurately at 12 points. Consequently, a bioharmonic spline interpolation algorithm was used to interpolate the h1 amplitude data obtained from the horizontal and vertical points, yielding 651 (31 × 21) h1 amplitude data. The 651 data points were converted into a heat map to intuitively calculate the pulse width. The pulse width was calculated by multiplying the number of grids on the vertical axis with the unit length of the grid. The pulse width was determined by TCM doctors to verify the pulse width measurement accuracy. Meanwhile, a color Doppler ultrasound examination of the volunteers' radial arteries was performed and the intravascular meridian widths of the radial artery compared with the calculated pulse widths to determine the reliability. RESULTS: The pulse width determined using the maximal h1 amplitude method was comparable with the radial artery intravascular meridian widths measured using color Doppler ultrasound. The h1 amplitude was higher in the high blood pressure group and the pulse width was greater. CONCLUSIONS: The pulse width determined using the maximal h1 amplitude was objective and accurate. Comparison between the pulse widths of the normal and high blood pressure groups verified the reliability of the method.

Effects of the Prolong Life With Nine Turn-Method Qigong on Fatigue, Insomnia, Anxiety, and Gastrointestinal Disorders in Patients With Chronic Fatigue Syndrome: Protocol for a Randomized Controlled Trial.

BACKGROUND: Chronic fatigue syndrome (CFS) is a debilitating multisystem disorder that can lead to various pathophysiological abnormalities and symptoms, including insomnia, gastrointestinal disorders, and anxiety. Due to the side effects of currently available drugs, there is a growing need for safe and effective nondrug therapies. The Prolong Life With Nine Turn (PLWNT) Qigong method is a system of mind-body exercise with restorative benefits that can alleviate the clinical symptoms of CFS and impart a significant inhibitory effect. Various studies have proven the treatment efficacy of PLWNT; however, the impact on insomnia, gastrointestinal disorders, and anxiety in patients with CFS has not yet been investigated. OBJECTIVE: This study aims to evaluate the efficacy and safety of the PLWNT method in terms of its effects on fatigue, insomnia, anxiety, and gastrointestinal symptoms in patients with CFS. METHODS: We will conduct a randomized, analyst-blinded, parallel-controlled trial with a 12-week intervention and 8-week follow-up. A total of 208 patients of age 20-60 years will be recruited. The patients will be randomly divided into a PLWNT Qigong exercise group (PLWNT Group) and a control group treated with cognitive behavioral therapy at a ratio of 1:1. Participants from the treatment groups will be taught by a highly qualified professor at the Shanghai University of Traditional Chinese Medicine once a week and will be supervised via web during the remaining 6 days at home, over 12 consecutive weeks. The primary outcome will be the Multidimensional Fatigue Inventory 20, while the secondary outcomes include the Pittsburgh Sleep Quality Index, Gastrointestinal Symptom Rating Scale, Hospital Anxiety and Depression Scale, functional magnetic resonance imaging, gut microbiota, and peripheral blood. RESULTS: The study was approved by the ethics committee of Shanghai Municipal Hospital of Traditional Chinese Medicine in March 2022 (Ethics Approval Number 2022SHL-KY-05). Recruitment started in July 2022. The intervention is scheduled to be completed in December 2024, and data collection will be completed by the end of January 2025. Over the 3-year recruitment period, 208 participants will be recruited. Data management is still in progress; therefore, data analysis has yet to be performed. CONCLUSIONS: This randomized trial will evaluate the effectiveness of the PLWNT method in relieving fatigue, insomnia, anxiety, and gastrointestinal symptoms in patients with CFS. If proven effective, it will provide a promising alternative intervention for patients with CFS. TRIAL REGISTRATION: China Clinical Trials Registry ChiCTR2200061229; https://www.chictr.org.cn/showproj.html?proj=162803. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/53347.

Study on the difference and regularity of tongue images in 309 patients with different pathological stages of non-small cell lung cancer.

BACKGROUND: Tongue diagnosis is a crucial traditional Chinese medicine (TCM) inspection method for TCM syndrome differentiation and treatment. OBJECTIVE: The primary research focus was on tongue image characteristic parameters of patients with non-small cell lung cancer (NSCLC). Analysis of the tongue image parameters of various pathological stages of NSCLC provides technical support for establishing an integrated Chinese and Western auxiliary diagnosis and efficacy evaluation medicine system for lung cancer that integrates tongue image features. METHODS: Tongue image characteristics of 309 patients with NSCLC and 206 controls were collected and analyzed clinically. The T-test or rank sum test and logistic regression analysis were applied to analyze the characteristics of tongue image indicators of different pathological stages of NSCLC. RESULTS: There were differences in tongue image characteristics in the NSCLC group compared to the control group. The tongue quality and brightness of the tongue coating in the NSCLC group increased, the red component was reduced, the tongue coating thickened, and the yellow component increased compared to the healthy control group. A comparison of tongue image indexes of NSCLC in different pathological stages showed that stage IV had lower TB-b and higher TB-a than stage I. In addition, stage IV had lower TB-b than stage II + III, showing an increase in the blue and red components of the tongue in stage IV and the appearance of cyanotic tongue features. CONCLUSION: The tongue image characteristics of NSCLC patients differed from those of the control group. Tongue imaging indicators can reflect the characteristics of tongue images of patients with NSCLC. The tongue image characteristics of patients with stage IV lung cancer are bluish and purple compared with those with stage I, II, and III. It is suggested that the tongue's image characteristics can be used as a reference for the pathological classification of NSCLC and judgment of the disease process.

Core characteristics of sublingual veins analysis and its relationship with hypertension.

BACKGROUND: The sublingual vein (SV) is a specialized diagnostic method used in Traditional Chinese Medicine (TCM). Despite its ability to objectively reflect blood flow, SV is often overlooked in clinical practice. OBJECTIVE: This study aims to analyze the core characteristics of SV and investigate the in-depth relationship between its digital characteristics and hypertension. The goal is to find a link between SV and hypertension and break out of the current situation. METHODS: Modern digital analysis techniques were applied to the traditional SV diagnostic theory. In a controlled study with 204 participants, the digital characteristics of SV were documented using TFDA-1, and its color value was analyzed using TDAS. Morphological characteristics of SV, such as trunklength, width, and tortuosity, were examined by combining computer vision with expert interpretation. This involved the application of automatic ranging methods and a rectangular approximation algorithm, which are novel approaches in the field of TCM. The t-test and Mann-Whitney U test were used to analyze the digital characteristics of SV in hypertension. Binary logistic regression and neural network models were established using machine learning to explore the deep relationship between SV characteristics and hypertension. RESULTS: There was a significant difference of the tortuosity of SV between the two groups (Z=-2.629, p= 0.009). The results revealed thick width of SV (OR = 2.64, 95% CI: 1.02-6.79) was the risk factor for hypertension. Addition of SV characteristics improved overall percent correct for hypertension prediction to 80%. CONCLUSION: TCM method of diagnosis of SV has been greatly expanded in terms of technical means, and the close relationship between SV and hypertension has been found in clinical data.

Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM approach.

Subhealth is a transitional state between health and disease, and it can be detected through routine physical check-ups. However, the complexity and diversity of physical examination items and the difficulty of quantifying subhealth manifestations are the main problems that hinder its treatment. The aim of this study was to systematically investigate the physical examination performance of the subhealthy population and further explore the deeper relationships between indicators. Indicators were obtained for 878 subjects, including basic information, Western medicine indicators, inquiries of traditional Chinese medicine and sublingual vein (SV) characteristics. Statistical differences were analysed using R software. To explore the distribution of symptoms and symptom clusters in subhealth, partial least squares-structural equation modelling (PLS-SEM) was applied to the subhealth physical examination index, and a structural model was developed to verify whether the relationship chain between the latent variables was reasonable. Finally, the reliability and validity of the PLS-SE model were assessed. The most common subclinical clinical symptoms were limb soreness (37.6%), fatigue (31.6%), shoulder and neck pain (30.5%) and dry eyes (29.2%). The redness of the SV in the subhealthy group was paler than that in the healthy group (p < 0.001). This study validates the establishment of the directed acyclic relationship chain in the subhealthy group: the path from routine blood tests to lipid metabolism (t = 7.878, p < 0.001), the path from lipid metabolism to obesity (t = 8.410, p < 0.001), the path from obesity to SV characteristics (t = 2.237, p = 0.025), and the path from liver function to SV characteristics (t = 2.215, p = 0.027). The innovative application of PLS-SEM to the study of subhealth has revealed the existence of a chain of relationships between physical examination indicators, which will provide a basis for further exploration of subhealth mechanisms and causal inference. This study has identified the typical symptoms of subhealth, and their early management will help to advance the treatment of diseases.

A lung cancer risk warning model based on tongue images.

Objective: To investigate the tongue image features of patients with lung cancer and benign pulmonary nodules and to construct a lung cancer risk warning model using machine learning methods. Methods: From July 2020 to March 2022, we collected 862 participants including 263 patients with lung cancer, 292 patients with benign pulmonary nodules, and 307 healthy subjects. The TFDA-1 digital tongue diagnosis instrument was used to capture tongue images, using feature extraction technology to obtain the index of the tongue images. The statistical characteristics and correlations of the tongue index were analyzed, and six machine learning algorithms were used to build prediction models of lung cancer based on different data sets. Results: Patients with benign pulmonary nodules had different statistical characteristics and correlations of tongue image data than patients with lung cancer. Among the models based on tongue image data, the random forest prediction model performed the best, with a model accuracy of 0.679 ± 0.048 and an AUC of 0.752 ± 0.051. The accuracy for the logistic regression, decision tree, SVM, random forest, neural network, and naïve bayes models based on both the baseline and tongue image data were 0.760 ± 0.021, 0.764 ± 0.043, 0.774 ± 0.029, 0.770 ± 0.050, 0.762 ± 0.059, and 0.709 ± 0.052, respectively, while the corresponding AUCs were 0.808 ± 0.031, 0.764 ± 0.033, 0.755 ± 0.027, 0.804 ± 0.029, 0.777 ± 0.044, and 0.795 ± 0.039, respectively. Conclusion: The tongue diagnosis data under the guidance of traditional Chinese medicine diagnostic theory was useful. The performance of models built on tongue image and baseline data was superior to that of the models built using only the tongue image data or the baseline data. Adding objective tongue image data to baseline data can significantly improve the efficacy of lung cancer prediction models.

Parameter study on characteristic pulse diagram of polycystic ovary syndrome based on logistic regression analysis.

This study aimed to explore the parameters of the independent predictive characteristic pulse diagram of polycystic ovary syndrome (PCOS) by analysing the pulse characteristics between healthy women and the PCOS group. A total of 278 women were recruited for this study. Pulse wave parameters were collected by the pulse spectrum analyser. The single-factor analysis of the pulse diagram parameters was used to identify significant indicators, and the logistic regression analysis was carried out on the above indicators with statistical differences to obtain independent predictors. According to the single-factor and multi-factor analyses, h1, h5, h3/h1, t, t1 and t5 were independent predictors of PCOS diagnosis. The results showed that PCOS patients had a faster heart rate, decreased left ventricular systolic function and decreased aortic compliance compared to healthy individuals. These findings suggested that the characteristic pulse parameters screened out are valuable for the diagnosis of PCOS.IMPACT STATEMENTWhat is already known on this subject? Polycystic ovary syndrome (PCOS) is a common gynecological reproductive endocrine and metabolic disease, which is significant for screening and early intervention in the disease. However, due to the lack of pulse's diagnostic evidence of PCOS, there is still an unknown area in the research on the correlation between PCOS and pulse diagram parameters.What do the results of this study add? This study fills the gap between the research on PCOS and pulse wave. The study also shows that the pulse characteristic parameters h1, h5, h3/h1, t, t1, and t5 are independent predictors of PCOS, suggesting that the patients have a higher heart rate, lower ventricular systolic function, and aortic compliance than healthy individuals.What are the implications of these findings for clinical practice and/or further research? Prominent risk factors for pulse parameters associated with the occurrence of PCOS facilitate early screening and diagnosis of the disease. The objectification of pulse diagnosis helps to establish a health management model, which can be used for the accurate assessment and treatment of PCOS by traditional Chinese medicine (TCM). It provides a clinical reference for the study of pulse diagnosis objectification.

Tongue features of patients with granulomatous lobular mastitis.

Traditional Chinese tongue diagnosis plays an irreplaceable role in disease diagnosis. This study aimed to describe the tongue characteristics of patients with granulomatous lobular mastitis (GLM). Forty GLM patients and 40 non-GLM controls were evaluated using the Traditional Chinese Medicine subjective clinical interpretation and a TDA-1 Tongue Diagnostic and Analysis system. The associations between the image features of the tongue body and coating and the profiling of immune-inflammatory parameters were analyzed. GLM patients were prone to a reddish tongue bodies with thick, white, and greasy coatings. Thick and greasy tongue coating features are risk factors for GLM. GLM patients had higher levels of white blood cells (WBC), platelets, C-reactive protein, interleukin-2, and transforming growth factor-ß (TGF-ß) than non-GLM controls (P < .05). Also, tongue coating contrast and entropy values were significantly correlated with WBC or TGF-ß levels in GLM patients (r < -0.310 and P < .05). We demonstrated that the hot evil and phlegm-dampness constitutions are the main characteristics of GLM. This might provide a reference for GLM diagnosis.

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup.

Background: Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods: A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results: The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion: The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

A multi-step approach for tongue image classification in patients with diabetes.

BACKGROUND: In China, diabetes is a common, high-incidence chronic disease. Diabetes has become a severe public health problem. However, the current diagnosis and treatment methods are difficult to control the progress of diabetes. Traditional Chinese Medicine (TCM) has become an option for the treatment of diabetes due to its low cost, good curative effect, and good accessibility. OBJECTIVE: Based on the tongue images data to realize the fine classification of the diabetic population, provide a diagnostic basis for the formulation of individualized treatment plans for diabetes, ensure the accuracy and consistency of the TCM diagnosis, and promote the objective and standardized development of TCM diagnosis. METHODS: We use the TFDA-1 tongue examination instrument to collect the tongue images of the subjects. Tongue Diagnosis Analysis System (TDAS) is used to extract the TDAS features of the tongue images. Vector Quantized Variational Autoencoder (VQ-VAE) extracts VQ-VAE features from tongue images. Based on VQ-VAE features, K-means clustering tongue images. TDAS features are used to describe the differences between clusters. Vision Transformer (ViT) combined with Grad-weighted Class Activation Mapping (Grad-CAM) is used to verify the clustering results and calculate positioning diagnostic information. RESULTS: Based on VQ-VAE features, K-means divides the diabetic population into 4 clusters with clear boundaries. The silhouette, calinski harabasz, and davies bouldin scores are 0.391, 673.256, and 0.809, respectively. Cluster 1 had the highest Tongue Body L (TB-L) and Tongue Coating L (TC-L) and the lowest Tongue Coating Angular second moment (TC-ASM), with a pale red tongue and white coating. Cluster 2 had the highest TC-b with a yellow tongue coating. Cluster 3 had the highest TB-a with a red tongue. Group 4 had the lowest TB-L, TC-L, and TB-b and the highest Per-all with a purple tongue and the largest tongue coating area. ViT verifies the clustering results of K-means, the highest Top-1 Classification Accuracy (CA) is 87.8%, and the average CA is 84.4%. CONCLUSIONS: The study organically combined unsupervised learning, self-supervised learning, and supervised learning and designed a complete diabetic tongue image classification method. This method does not rely on human intervention, makes decisions based entirely on tongue image data, and achieves state-of-the-art results. Our research will help TCM deeply participate in the individualized treatment of diabetes and provide new ideas for promoting the standardization of TCM diagnosis.

The Qigong of Prolong Life With Nine Turn Method Relieve Fatigue, Sleep, Anxiety and Depression in Patients With Chronic Fatigue Syndrome: A Randomized Controlled Clinical Study.

Background: Chronic fatigue syndrome (CFS) is a complex disease of unknown etiology and mechanism. The purpose of this study was to investigate the effect of Prolong Life with Nine Turn Method (PLWNT) Qigong exercise on CFS focusing on fatigue, sleep quality, depression, and anxiety. Methods: A total of 90 participants diagnosed with CFS were randomly assigned into two parallel groups: PLWNT and cognitive behavioral therapy (CBT). The participants in the PLWNT or CBT group participated in qigong exercise or cognitive behavior education program, respectively, once a week in-person and were supervised online during the remaining 6 days at home, over 12 consecutive weeks. The primary outcome was fatigue (Multi-dimensional Fatigue Inventory 20 [MFI-20]), and secondary outcomes were sleep quality (Pittsburgh Sleep Quality Index [PSQI]), anxiety, depression (Hospital Anxiety and Depression Scale [HADS]), and changes in the Neuropeptide Y (NPY) of peripheral blood. Results: The within-group comparisons of the PLWNT and CBT groups revealed significant improvement in both groups in MFI-20, PSQI, and HADS scores (P < 0.05). No significant difference were found between the PLWNT and CBT groups, even though the effective rate of the PLWNT group was 62.22%, which is slightly than 50.00% of the CBT group. The fatigue scores in the PLWNT group were positively correlated with sleep degree (r = 0.315) and anxiety degree (r = 0.333), only anxiety degree (r = 0.332) was found to be positively correlated with fatigue in the CBT group. The analysis of peripheral blood showed that NPY decreased after PLWNT intervention but increased significantly in the CBT. Conclusion: The PLWNT qigong exercise has potential to be an effective rehabilitation method for CFS symptoms including fatigue, sleep disturbance, anxiety, and depression. Future studies should expand study sample size for in-depth investigation to determine the optimal frequency and intensity of PLWNT qigong intervention in CFS patients. The study was registered in the ClinicalTrials.gov database on April 12, 2018, with registration number NCT03496961.

Research of the Distribution of Tongue Features of Diabetic Population Based on Unsupervised Learning Technology.

Background: The prevalence of diabetes increases year by year, posing a severe threat to human health. Current treatments are difficult to prevent the progression of diabetes and its complications. It is imperative to carry out individualized treatment of diabetes, but current diagnostic methods are difficult to specify an individualized treatment plan. Objective: Clarify the distribution law of tongue features of the diabetic population, and provide the diagnostic basis for individualized treatment of traditional Chinese medicine (TCM) in the treatment of diabetes. Methods: We use the TFDA-1 tongue diagnosis instrument to collect tongue images of people with diabetes and accurately calculate the color features, texture features, and tongue coating ratio features through the Tongue Diagnosis Analysis System (TDAS). Then, we used K-means and Self-organizing Maps (SOM) networks to analyze the distribution of tongue features in diabetic people. Statistical analysis of TDAS features was used to identify differences between clusters. Results: The silhouette coefficient of the K-means clustering result is 0.194, and the silhouette coefficient of the SOM clustering result is 0.127. SOM Cluster 3 and Cluster 4 are derived from K-means Cluster 1, and the intersections account for (76.7% 97.5%) and (22.3% and 70.4%), respectively. K-means Cluster 2 and SOM Cluster 1 are highly overlapping, and the intersection accounts for the ratios of 66.9% and 95.0%. K-means Cluster 3 and SOM Cluster 2 are highly overlaid, and the intersection ratio is 94.1% and 82.1%. For the clustering results of K-means, TB-a and TC-a of Cluster 3 are the highest (P < 0.001), TB-a of Cluster 2 is the lowest (P < 0.001), and TB-a of Cluster 1 is between Cluster 2 and Cluster 3 (P < 0.001). Cluster 1 has the highest TB-b and TC-b (P < 0.001), Cluster 2 has the lowest TB-b and TC-b (P < 0.001), and TB-b and TC-b of Cluster 3 are between Cluster 1 and Cluster 2 (P < 0.001). Cluster 1 has the highest TB-ASM and TC-ASM (P < 0.001), Cluster 3 has the lowest TB-ASM and TC-ASM (P < 0.001), and TB-ASM and TC-ASM of Cluster 2 are between the Cluster 1 and Cluster 3 (P < 0.001). CON, ENT, and MEAN show the opposite trend. Cluster 2 had the highest Per-all (P < 0.001). SOM divides K-means Cluster 1 into two categories. There is almost no difference in texture features between Cluster 3 and Cluster 4 in the SOM clustering results. Cluster 3's TB-L, TC-L, and Per-all are lower than Cluster 4 (P < 0.001), Cluster 3's TB-a, TC-a, TB-b, TC-b, and Per-part are higher than Cluster 4 (P < 0.001). Conclusions: The precise tongue image features calculated by TDAS are the basis for characterizing the disease state of diabetic people. Unsupervised learning technology combined with statistical analysis is an important means to discover subtle changes in the tongue features of diabetic people. The machine vision analysis method based on unsupervised machine learning technology realizes the classification of the diabetic population based on fine tongue features. It provides a diagnostic basis for the designated diabetes TCM treatment plan.

Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders.

Study on the objectivity of pulse diagnosis is inseparable from the instruments to obtain the pulse waves. The single-pulse diagnostic instrument is relatively mature in acquiring and analysing pulse waves, but the pulse information captured by single-pulse diagnostic instrument is limited. The sensor arrays can simulate rich sense of the doctor's fingers and catch multipoint and multiparameter array signals. How to analyse the acquired array signals is still a major problem in the objective research of pulse diagnosis. The goal of this study was to establish methods for analysing arrayed pulse waves and preliminarily apply them in hypertensive disorders. While a sensor array can be used for the real-time monitoring of twelve pulse wave channels, for each subject in this study, only the pulse wave signals of the left hand at the "guan" location were obtained. We calculated the average pulse wave (APW) per channel over a thirty-second interval. The most representative pulse wave (MRPW) and the APW were matched by their correlation coefficient (CC). The features of the MRPW and the features that corresponded to the array pulse volume (APV) parameters were identified manually. Finally, a clinical trial was conducted to detect these feature performance indicators in patients with hypertensive disorders. The independent-samples t-tests and the Mann-Whitney U-tests were performed to assess the differences in these pulse parameters between the healthy and hypertensive groups. We found that the radial passage (RP) APV h1, APV h3, APV h4, APV h3/h1 (P < 0.01), and APV h4/h1 (P < 0.05) were significantly higher in the hypertensive group than in the healthy group; the intermediate passage (IP) APV h4, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) and the mean APV h3, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) were significantly higher in the hypertensive group than in the healthy group, and the ulnar passage (UP) APV h4/h1 (P < 0.05) was clearly elevated in the hypertensive group. These results provide a preliminary validation of this novel approach for determining the APV by arrayed pulse wave analysis. In conclusion, we identified effective indicators of hypertensive vascular function. Traditional Chinese medicine (TCM) pulses comprise multidimensional information, and a sensor array could provide a better indication of TCM pulse characteristics. In this study, the validation of the arrayed pulse wave analysis demonstrates that the APV can reliably mirror TCM pulse characteristics.

Correlation Analysis of Data of Tongue and Pulse in Patients With Disease Fatigue and Sub-health Fatigue.

Fatigue is one of the most common subjective symptoms of abnormal health state, there is still no reliable and stable evaluation method to distinguish disease fatigue and non-disease fatigue. Studies have shown that tongue diagnosis and pulse diagnosis are the reflection of overall state of the body. This study aims to explore the distribution rules and correlation of data of tongue and pulse in population with disease fatigue and sub-health fatigue and provide a new method of clinical diagnosis of fatigue from the perspective of tongue diagnosis and pulse diagnosis. In this study, a total of 736 people were selected and divided into healthy controls (n = 250), sub-health fatigue group (n = 242), and disease fatigue group (n = 244). TFDA-1 tongue diagnosis instrument and PDA-1 pulse diagnosis instrument were used to collect tongue image and sphygmogram, simple correlation analysis and canonical correlation analysis were used to analyze the correlation of tongue and pulse data about the two groups of fatigue people. The study had shown that tongue and pulse data could provide a certain reference for the diagnosis of different types of fatigue, tongue and pulse data in disease fatigue and sub-health fatigue population had different distribution rules, and there was a simple correlation and canonical correlation in the disease fatigue population, the coefficient of canonical correlation was .649 (P <.05).

Efficacy and Safety of Ejiao (Asini Corii Colla) in Women With Blood Deficient Symptoms: A Randomized, Double-Blind, and Placebo-Controlled Clinical Trial.

Equus asinus L [Equidae; Asini Corii Colla] (donkey-hide gelatin, Ejiao), a well-known traditional Chinese medicine, has been widely used to nourish the blood, especially for women. The aim of this study was to assess the efficacy and safety of Ejiao in blood-deficient patients. A total of 210 participants were recruited and randomly allocated into the placebo control group and Ejiao-treated group (6 g/day). The primary outcomes on the efficacy of Ejiao included traditional Chinese medicine symptom scores, blood indicators, and SF-36. The secondary outcomes were changes in fireness and safety evaluation. Results showed that Ejiao treatment for 8 weeks had significantly improved dizziness symptoms. Among the tested 24 blood biochemical parameters, the hematocrit and red blood cell numbers decreased in the placebo control group, but decreased significantly less in the Ejiao treatment group. The white blood cell and neutrophil counts increased in the Ejiao group but were within the normal range. In addition, the quality of life improved as the scores in SF-36 domains were significantly higher in the Ejiao group. At the same time, there was no significant change in the fire-heat symptoms score or other safety parameters. Considering all these, our study showed that Ejiao has a promising effect in women suffering from blood deficiency without obvious adverse effects.

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning.

OBJECTIVE: To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. METHODS: We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome (n = 163), patients with Yin deficiency syndrome (n = 174), and healthy controls (n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. RESULTS: Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. CONCLUSIONS: It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

Acupuncture combined with western medicine for the treatment of hypertension: A protocol for an updated systematic review and meta-analysis.

BACKGROUND: Hypertension is a kind of cardiovascular syndrome with the main clinical manifestation of continuous increase of systemic arterial blood pressure. Hypertension coexists with other cardiovascular risk factors and is an important risk factor for cardiovascular and cerebrovascular diseases. Acupuncture is an important part of Traditional Chinese Medicine intervention. The antihypertensive effect of acupuncture on hypertension is based on the neuroendocrine system, characterized by multichannel and multitarget. This study aims to provide latest and updated proof of systematic review to assess the effectiveness and safety of acupuncture for hypertension. METHODS: We will systematically search 9 databases from their inceptions to February 2021. Only randomized controlled trials of acupuncture combined with western medicine in the treatment of hypertension will meet the inclusion criteria. The main outcome measures we focus on include clinical efficacy, syndrome efficacy, Traditional Chinese Medicine syndrome score, diastolic and systolic blood pressure changes, blood pressure variability, heart rate variability, pulse rate variability, and adverse reactions. The research screening, data extraction, and risk of bias assessment will be employed by 2 reviewers independently, and disagreement will be decided by a third senior reviewer. The Revman 5.3 software will be used for meta-analysis. The confidence of proof will be rated adopting grading of recommendations assessment, development and evaluation tool and methodological quality of this research will be assessed using assessment of multiple systematic reviews-2 and risk of bias in systematic reviews. The publication quality will be evaluated by preferred reporting items for systematic reviews and meta-analyses (PRISMA). RESULTS: This systematic review (SR) will provide evidence-based medical evidence for hypertension therapy by acupuncture combined with western medicine and we will submit the findings of this SR for peer-review publication. CONCLUSIONS: This SR will provide latest and updated summary proof for assessing the effectiveness and safety of acupuncture for hypertension. REGISTRATION NUMBER: INPLASY 202150047.

A tongue features fusion approach to predicting prediabetes and diabetes with machine learning.

BACKGROUND: Diabetics has become a serious public health burden in China. Multiple complications appear with the progression of diabetics pose a serious threat to the quality of human life and health. We can prevent the progression of prediabetics to diabetics and delay the progression to diabetics by early identification of diabetics and prediabetics and timely intervention, which have positive significance for improving public health. OBJECTIVE: Using machine learning techniques, we establish the noninvasive diabetics risk prediction model based on tongue features fusion and predict the risk of prediabetics and diabetics. METHODS: Applying the type TFDA-1 Tongue Diagnosis Instrument, we collect tongue images, extract tongue features including color and texture features using TDAS, and extract the advanced tongue features with ResNet-50, achieve the fusion of the two features with GA_XGBT, finally establish the noninvasive diabetics risk prediction model and evaluate the performance of testing effectiveness. RESULTS: Cross-validation suggests the best performance of GA_XGBT model with fusion features, whose average CA is 0.821, the average AUROC is 0.924, the average AUPRC is 0.856, the average Precision is 0.834, the average Recall is 0.822, the average F1-score is 0.813. Test set suggests the best testing performance of GA_XGBT model, whose average CA is 0.81, the average AUROC is 0.918, the average AUPRC is 0.839, the average Precision is 0.821, the average Recall is 0.81, the average F1-score is 0.796. When we test prediabetics with GA_XGBT model, we find that the AUROC is 0.914, the Precision is 0.69, the Recall is 0.952, the F1-score is 0.8. When we test diabetics with GA_XGBT model, we find that the AUROC is 0.984, the Precision is 0.929, the Recall is 0.951, the F1-score is 0.94. CONCLUSIONS: Based on tongue features, the study uses classical machine learning algorithm and deep learning algorithm to maximum the respective advantages. We combine the prior knowledge and potential features together, establish the noninvasive diabetics risk prediction model with features fusion algorithm, and detect prediabetics and diabetics noninvasively. Our study presents a feasible method for establishing the association between diabetics and the tongue image information and prove that tongue image information is a potential marker which facilitates effective early diagnosis of prediabetics and diabetics.

Digital tongue image analyses for health assessment.

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.