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This study aims at introducing a method for individual agreement evaluation to identify the discordant raters from the experts’ group. We exclude those experts and decide the best experts selection method, so as to improve the reliability of the constructed tongue image database based on experts’ opinions. Fifty experienced experts from the TCM diagnostic field all over China were invited to give ratings for 300 randomly selected tongue images. Gwet’s AC1 (first-order agreement coefficient) was used to calculate the interrater and intrarater agreement. The optimization of the interrater agreement and the disagreement score were put forward to evaluate the external consistency for individual expert. The proposed method could successfully optimize the interrater agreement. By comparing three experts’ selection methods, the interrater agreement was, respectively, increased from 0.53 [0.32-0.75] for original one to 0.64 [0.39-0.80] using method A (inclusion of experts whose intrarater agreement>0.6), 0.69 [0.63-0.81] using method B (inclusion of experts whose disagreement score=“0”), and 0.76 [0.67-0.83] using method C (inclusion of experts whose intrarater agreement>0.6& disagreement score=“0”). In this study, we provide an estimate of external consistency for individual expert, and the comprehensive consideration of both the internal consistency and the external consistency for each expert would be superior to either one in the tongue image construction based on expert opinions. [ABSTRACT FROM AUTHOR]

Background and Goal. The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. Methods. Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongue imaging device in TIFF format through ICC profile correction. Then we compare the mean value of L*a*b* of different tongue colors and evaluate the effect of the tongue color classification by machine learning methods. Results. The L*a*b* values of the five tongue colors are statistically different. Random forest method has a better performance than SVM in classification. SMOTE algorithm can increase classification accuracy by solving the imbalance of the varied color samples. Conclusions. At the premise of standardized tongue acquisition and color reproduction, preliminary objectification of tongue color classification in Traditional Chinese Medicine (TCM) is feasible. [ABSTRACT FROM AUTHOR]

Precisely controlling the product selectivity of a reaction is an important objective in organic synthesis. α‐Ketoamides are vital intermediates in chemical transformations and privileged motifs in numerous drugs, natural products, and biologically active molecules. The selective synthesis of α‐ketoamides from feedstock chemicals in a safe and operationally simple manner under mild conditions is a long‐standing catalysis challenge. Herein, an unprecedented TBD‐switched Pd‐catalyzed double isocyanide insertion reaction for assembling ketoamides in aqueous DMSO from (hetero)aryl halides and pseudohalides under mild conditions is reported. The effectiveness and utility of this protocol are demonstrated by its diverse substrate scope (93 examples), the ability to late‐stage modify pharmaceuticals, scalability to large‐scale synthesis, and the synthesis of pharmaceutically active molecules. Mechanistic studies indicate that TBD is a key ligand that modulates the Pd‐catalyzed double isocyanide insertion process, thereby selectively providing the desired α‐ketoamides in a unique manner. In addition, the imidoylpalladium(II) complex and α‐ketoimine amide are successfully isolated and determined by X‐ray analysis, confirming that they are probable intermediates in the catalytic pathway. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aims at exploring the cardiovascular pathophysiological mechanism of TCM (traditional Chinese medicine) pulse by detecting the correlation between radial artery pulse wave variables and pulse wave velocity/echocardiographic parameters. Two hundred Chinese subjects were enrolled in this study, which were grouped into health control group, hypertension group, and hypertensive heart disease group. Physical data obtained in this study contained TCM pulse images at "Guan" position of the left hand, pulse wave velocity, and echocardiographic parameters. Linear and stepwise regression analysis was performed to assess the association of radial artery pulse wave variables with pulse wave velocity and echocardiographic parameters in the total population and in each different group. After adjusting for related confounding factors, decrease of t1, t5 and increase of h1, h3/h1 were statistically associated with arterial stiffness in the total population (P<0.05). Moreover, the correlation study in each group showed that the decrease of both t3 and h5 was also related to arterial stiffness (P<0.05). In terms of echocardiographic parameters, the height of dicrotic wave indicated by h5 was the most relevant pulse wave variable. For the health control, h5 was negatively associated with interventricular septal thickness (VST) and left ventricular posterior wall thickness (PWT) (P<0.05), while for the hypertension population and those with target-organ damage to heart, increase of h5 might be associated with decrease of ejection fraction (EF) and increase of all the remaining echocardiographic parameters especially for left ventricular end-systolic diameter (LVDs) and Left ventricular end-diastolic diameter (LVDd) (P<0.05). In conclusion, we found radial artery pulse wave variables were in association with the arterial stiffness and echocardiographic changes in hypertension, which would provide an experimental basis for cardiovascular pathophysiological mechanism of radial artery pulse wave variables. [ABSTRACT FROM AUTHOR]

Background and Objective. Fatigue is a subjective symptom which is hard to quantify, it is prevalent in the subhealth and disease population, and there is still no accurate and stable method to distinguish disease fatigue from subhealth fatigue. Tongue diagnosis and pulse diagnosis are the reflection of the overall state of the body, and the modern research of tongue diagnosis and pulse diagnosis has made great progress. This study aims to explore the distribution rules of tongue and pulse data in a disease fatigue and subhealth fatigue population and evaluate the contribution rate of tongue and pulse data to fatigue diagnosis through modeling. Methods. The Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument were used to collect the tongue image and sphygmogram of the subhealth fatigue population (n = 252) and disease fatigue population (n = 1160), and we mainly analyzed the tongue and pulse characteristics and constructed the classification model by using the logistic regression method. Results. The results showed that subhealth fatigue people and disease fatigue people had different characteristics of tongue and pulse, and the logistic regression model based on tongue and pulse data had a good classification effect. The accuracies of models of healthy controls and subhealth fatigue, subhealth fatigue and disease fatigue, and healthy controls and disease fatigue were 68.29%, 81.18%, and 84.73%, and the AUC was 0.698, 0.882, and 0.924, respectively. Conclusion. This study provided a new noninvasive method for the fatigue diagnosis from the perspective of objective tongue and pulse data, and the modern tongue diagnosis and pulse diagnosis have good application prospects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM. Methods: Machine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score. Results: The experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA. Conclusions: Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible. [ABSTRACT FROM AUTHOR]

Background: Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM.Methods: Machine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score.Results: The experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA.Conclusions: Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible. [ABSTRACT FROM AUTHOR]

Objective. In this study, machine learning was utilized to classify and predict pulse wave of hypertensive group and healthy group and assess the risk of hypertension by observing the dynamic change of the pulse wave and provide an objective reference for clinical application of pulse diagnosis in traditional Chinese medicine (TCM). Method. The basic information from 450 hypertensive cases and 479 healthy cases was collected by self-developed H20 questionnaires and pulse wave information was acquired by self-developed pulse diagnostic instrument (PDA-1). H20 questionnaires and pulse wave information were used as input variables to obtain different machine learning classification models of hypertension. This method was aimed at analyzing the influence of pulse wave on the accuracy and stability of machine learning model, as well as the feature contribution of hypertension model after removing noise by K-means. Result. Compared with the classification results before removing noise, the accuracy and the area under the curve (AUC) had been improved. The accuracy rates of AdaBoost, Gradient Boosting, and Random Forest (RF) were 86.41%, 86.41%, and 85.33%, respectively. AUC were 0.86, 0.86, and 0.85, respectively. The maximum accuracy of SVM increased from 79.57% to 83.15%, and the AUC stability increased from 0.79 to 0.83. In addition, the features of importance on traditional statistics and machine learning were consistent. After removing noise, the features with large changes were h1/t1, w1/t, t, w2, h2, t1, and t5 in AdaBoost and Gradient Boosting (top10). The common variables for machine learning and traditional statistics were h1/t1, h5, t, Ad, BMI, and t2. Conclusion. Pulse wave-based diagnostic method of hypertension has significant value in reference. In view of the feasibility of digital-pulse-wave diagnosis and dynamically evaluating hypertension, it provides the research direction and foundation for Chinese medicine in the dynamic evaluation of modern disease diagnosis and curative effect. [ABSTRACT FROM AUTHOR]

Background and Objective. The same range of blood pressure values may reflect different vascular functions, especially in the elderly. Therefore, a single blood pressure value may not comprehensively reveal cardiovascular function. This study focused on identifying pulse wave features in the elderly that can be used to show functional differences when blood pressure values are in the same range. Methods. First, pulse data were preprocessed and pulse cycles were segmented. Second, time domain, higher-order statistics, and energy features of wavelet packet decomposition coefficients were extracted. Finally, useful pulse wave features were evaluated using a feature selection and classifier design. Results. A total of 6,075 pulse wave cycles were grouped into 3 types according to different blood pressure levels and each group was divided into 2 categories according to a history of hypertension. The classification accuracy of feature selection in the 3 groups was 97.91%, 95.24%, and 92.28%, respectively. Conclusion. Selected features could be appropriately used to analyze cardiovascular function in the elderly and can serve as the basis for research on a cardiovascular risk assessment model based on Traditional Chinese Medicine pulse diagnosis. [ABSTRACT FROM AUTHOR]