Objective Analysis of Traditional Chinese Medicine Syndrome Differentiation of Patients With Diabetes and Prediabetes: Protocol for a Nonrandomized, Exploratory, Observational Case-Control Study Using Digitalized Traditional Chinese Medicine Diagnostic Tools.

BACKGROUND: Diabetes and prediabetes are diagnosed differentially by Western and Chinese medicine. While Western medicine uses objective laboratory analysis of biochemical parameters to define the severity of diabetes and prediabetes, Chinese medicine uses a comprehensive approach that integrates observation, inquiry, pulse palpation, and tongue diagnosis. The medical information collected is then categorized into different syndromes. However, traditional methods of pulse and tongue diagnoses used to determine syndrome differentiation are highly subjective and skill dependent. OBJECTIVE: This study aims to identify the gap in conventional traditional Chinese medicine (TCM) diagnostic techniques for syndrome differentiation analysis using contemporary diagnostic devices. We devised a protocol for a nonrandomized, exploratory, observational case-control study with equal allocations in 5 arms to investigate the syndrome differentiation of diabetes and prediabetes. We hypothesize that the TCM syndrome differentiation of diabetes and prediabetes in the tropical climate may differ from that defined based on the Chinese demographic. We also speculate that the high-frequency spectral energy may reflect a difference in pulse wave intensity and density between the healthy and diabetes groups. METHODS: A total of 250 eligible participants will be equally assigned to 1 of 5 arms (healthy or subhealthy, prediabetes, diabetes, prediabetes with hypertension and dyslipidemia, and diabetes with hypertension and dyslipidemia). Participants aged 21-75 years, of any sex or race, and have been diagnosed with diabetes (fasting plasma glucose [FPG] of 7 mmol/L, or 2-hour plasma glucose [2hPG] of 11.1 mmol/L) or prediabetes (impaired FPG of 6.1-6.9 mmol/L, or impaired glucose tolerance with an 2hPG of 7.8-11 mmol/L) will be included. The Health Evaluation Questionnaire, Physical Activity Questionnaire, sugar intake assessment, Constitution in Chinese Medicine Questionnaire, radial pulse diagnosis, and tongue diagnosis will be performed in a single visit. ANOVA for continuous data and chi-square tests of independence will be used for categorical data assessments, with a level of P<.05 considered significant. RESULTS: The recruitment is in progress. We anticipate that the study will conclude in June 2025. As of July 15, 2024, we have enrolled 140 individuals. CONCLUSIONS: To the best of our knowledge, this is the first study to use contemporary TCM diagnostic instruments to map expert and empirical knowledge of TCM to its scientific equivalents for the purpose of evaluating the syndrome differentiation of diabetes. We designed this protocol with the exploratory goal to examine objectively the syndrome differentiation of patients with diabetes and those with prediabetes using TCM diagnostic technologies. The data collected and evaluated under standardized conditions using these contemporary diagnostic devices will exhibit a higher degree of stability, hence yielding dependable and unbiased results for syndrome differentiation. Thus, our findings may potentially increase the accuracy of identification, diagnosis, treatment, and prevention of diabetes and prediabetes through a system of targeted treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT05563090; https://clinicaltrials.gov/ct2/show/NCT05563090. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56024.

A Novel Tongue Coating Segmentation Method Based on Improved TransUNet.

BACKGROUND: As an important part of the tongue, the tongue coating is closely associated with different disorders and has major diagnostic benefits. This study aims to construct a neural network model that can perform complex tongue coating segmentation. This addresses the issue of tongue coating segmentation in intelligent tongue diagnosis automation. METHOD: This work proposes an improved TransUNet to segment the tongue coating. We introduced a transformer as a self-attention mechanism to capture the semantic information in the high-level features of the encoder. At the same time, the subtraction feature pyramid (SFP) and visual regional enhancer (VRE) were constructed to minimize the redundant information transmitted by skip connections and improve the spatial detail information in the low-level features of the encoder. RESULTS: Comparative and ablation experimental findings indicate that our model has an accuracy of 96.36%, a precision of 96.26%, a dice of 96.76%, a recall of 97.43%, and an IoU of 93.81%. Unlike the reference model, our model achieves the best segmentation effect. CONCLUSION: The improved TransUNet proposed here can achieve precise segmentation of complex tongue images. This provides an effective technique for the automatic extraction in images of the tongue coating, contributing to the automation and accuracy of tongue diagnosis.

S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine.

Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject's cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy.

Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies.

Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep learning (DL), specifically in computer vision (CV) and natural language processing (NLP), offer novel opportunities to modernize TCM by exploring the profound connotations of its theory. This review begins with an overview of the ML and DL methods employed in TCM; this is followed by practical instances of these applications. Furthermore, extensive discussions emphasize the mature integration of ML and DL in TCM, such as tongue diagnosis, pulse diagnosis, and syndrome differentiation treatment, highlighting their early successful application in the TCM field. Finally, this study validates the accomplishments and addresses the problems and challenges posed by the application and development of TCM powered by ML and DL. As ML and DL techniques continue to evolve, modern technology will spark new advances in TCM.

Application of intelligent tongue image analysis in Conjunction with microbiomes in the diagnosis of MAFLD.

Background: Metabolic associated fatty liver disease (MAFLD) is a widespread liver disease that can lead to liver fibrosis and cirrhosis. Therefore, it is essential to develop early diagnosic and screening methods. Methods: We performed a cross-sectional observational study. In this study, based on data from 92 patients with MAFLD and 74 healthy individuals, we observed the characteristics of tongue images, tongue coating and intestinal flora. A generative adversarial network was used to extract tongue image features, and 16S rRNA sequencing was performed using the tongue coating and intestinal flora. We then applied tongue image analysis technology combined with microbiome technology to obtain an MAFLD early screening model with higher accuracy. In addition, we compared different modelling methods, including Extreme Gradient Boosting (XGBoost), random forest, neural networks(MLP), stochastic gradient descent(SGD), and support vector machine(SVM). Results: The results show that tongue-coating Streptococcus and Rothia, intestinal Blautia, and Streptococcus are potential biomarkers for MAFLD. The diagnostic model jointly incorporating tongue image features, basic information (gender, age, BMI), and tongue coating marker flora (Streptococcus, Rothia), can have an accuracy of 96.39%, higher than the accuracy value except for bacteria. Conclusion: Combining computer-intelligent tongue diagnosis with microbiome technology enhances MAFLD diagnostic accuracy and provides a convenient early screening reference.

Noninvasive Diagnostic Technique for Nonalcoholic Fatty Liver Disease Based on Features of Tongue Images.

OBJECTIVE: To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images. METHODS: Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD. RESULTS: A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68-0.98]. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66-0.92) in validation set. CONCLUSIONS: The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.

Noninvasive Diagnostic Technique for Nonalcoholic Fatty Liver Disease Based on Features of Tongue Images / 中国结合医学杂志

OBJECTIVE@#To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images.@*METHODS@#Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD.@*RESULTS@#A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68-0.98]. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66-0.92) in validation set.@*CONCLUSIONS@#The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.

Research status and prospect of tongue image diagnosis analysis based on machine learning / 数字中医药（英文）

@#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 nuclear magnetic resonance imaging (MRI). These techniques have significantly enhanced the efficiency 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 intelligence 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 contemporary tongue diagnosis research. Therefore, the employment of machine learning techniques 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 image 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 combination 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.

Research on Objective Characteristics of Tongue Manifestation in 315 Patients with Coronary Heart Disease / 中医杂志

ObjectiveTo investigate the objective characteristics of tongue manifestations in patients with coronary heart disease （CHD）. MethodsA total of 315 participants with CHD were recruited in the CHD group， and 211 healthy participants who underwent physical examination were recruited as the healthy control group. In addition， according to the common comorbidities （primary hypertension， carotid atherosclerosis， type 2 diabetes mellitus， fatty liver， hyperlipidaemia， heart failure， and cerebral infarction） in 315 participants with CHD， each comorbidity was classified into a group of comorbidities with that disease and a group of non-comorbidities. Tongue images were captured using a TFDA-1 tongue diagnostic instrument to characterise the tongue body （TB） and tongue coating （TC）， comparing the RGB， HIS， and Lab colour spaces in the chromaticity index （R， red； G， green； B， blue； H， hue； I， intensity； S， saturation； L， lightness； a， red-green axis； b， yellow-blue axis）， the tongue coating thickness index （per-All）， contrast （CON）， angular second moment （ASM）， entropy （ENT）， and mean （MEAN） in texture metrics. ResultsCompared with the healthy control group， the characteristic indexes of tongue body in CHD group showed lower TB-R， TB-G， TB-B， TB-I， TB-L and higher TB-H， TB-b； and the characteristic indexes of tongue coating in CHD group showed lower TC-R， TC-B and higher TC-CON， TC-MEAN， TC-H， TC-b （P＜0.05 or P＜0.01）. Compared with non-combined primary hypertension group， CHD combined primary hypertension group showed higher per-All， TB-G， TB-L， and lower TB-a， TC-a （P＜0.05）； compared with the non-combined carotid atherosclerosis group， CHD combined carotid atherosclerosis group showed higher TB-CON， TB-ENT， TB-MEAN， and lower TB-ASM （P＜0.05 or P＜0.01）； compared with the non-combined type 2 diabetes mellitus group， CHD combined type 2 diabetes mellitus group showed lower per-All and higher TB-H （P＜0.05 or P＜0.01）； compared with the non-combined fatty liver group， CHD combined fatty liver group showed higher TB-CON， TB-MEAN， TB-ENT， and lower TB-ASM and TC-S （P＜0.05 or P＜0.01）； compared with the non-combined hyperlipidaemia group， CHD combined hyperlipidaemia group showed lower TB-S and TB-a （P＜0.05）； compared with non-combined heart failure group， CHD combined heart failure group showed lower TB-R， TB-G， TB-I， TB-L， and higher TB-a （P＜0.05 or P＜0.01）； compared with non-combined cerebral infarction group， CHD combined cerebral infarction group showed higher TC-CON， TC-ENT， TC-MEAN， and lower TC-ASM （P＜0.05 or P＜0.01）. ConclusionCompared to healthy individuals， patients with CHD tend to have darker tongue colours and rougher TC textures. Compared with non-comorbidity participants， those with primary hypertension tended to be lighter tongue colour and thicker tongue coating， those with carotid atherosclerosis had paler tongue body， those with type 2 diabetes mellitus had thinner tongue coating， those with fatty liver disease had paler tongue body and whiter tongue colour， those with hyperlipidaemia and heart failure had paler tongue colour， and those with cerebral infarction had rougher tongue texture.

Dark red tongue color formation caused by hyperglycemia is attributed to decreased blood flow of tongue tissue partially due to nuclear factor-kappa B pathway activation.

OBJECTIVE: To investigate the potential mechanisms underlying the dark red tongue color formation induced by hyperglycemia. METHODS: A high-fat diet and intraperitoneal injection of streptozotocin were used to establish a diabetes model. The color and blood flow of tongues were analyzed by the Tongue Diagnosis Analysis System and laser Doppler flowmetry, respectively. Inflammatory factors and adhesion factors were measured in the circulation and tongue tissue by an enzyme-linked immunosorbent assay. Western blotting was employed to evaluate nuclear factor-kappa B (NF-κB) p50 and inhibitor of kappa B kinase protein expression levels in the tongue. Then, the NF-κB inhibitor, pyrrolidine dithiocarbamic acid ammonium salt was utilized to repress NF-κB pathway activation to validate that the NF-κB pathway plays a key role in blood flow and dark red tongue color formation. RESULTS: The diabetic rats displayed a dark red tongue color that was accompanied by NF-κB pathway activation and decreased blood flow in the tongue. These effects could be reversed by the NF-κB inhibitor. CONCLUSIONS: Our investigation demonstrated that hyperglycemia led to dark red tongue color formation by decreasing blood flow in the tongue, which was partly due to NF-κB pathway activation.

Literature review and prospect on oral cognition and disease diagnosis and treatment between Han and Tang dynasties.

Chinese medicine entered a significant period from foundation to maturity between Han and Tang dynasties when the Chinese traditional stomatology was a key stage. Sorting and analysis of existing literature and research outcomes have showed that current research on stomatology between Han and Tang dynasties focuses on oral physiology, pathology, diagnosis and treatment, and health care. It also involves stomatology history and explanation of termino-logies related to mouth and teeth recorded in medical books, use of simple methods, and thinking with citation and analysis of literature simply listed and reasoning preliminarily deducted. From the macro perspective, current research has not unveiled the whole picture of stomatology between the two dynasties and left a series of key issues unresolved. Thus, new methods should be developed and employed to carry out medical research on stomatology between Han and Tang dynasties given that is has a prosperous future.

Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study.

OBJECTIVE: To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker. METHODS: Tongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, six classifiers, decision tree, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker. RESULTS: There were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. In the advanced NSCLC group, the number of indexes with statistically significant correlations between tongue feature and tumor marker was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. Support Vector Machine (SVM), decision tree, and logistic regression among the machine learning methods performed poorly in models with different stages of NSCLC. Neural network, random forest and naive bayes had better classification efficiency for the data set of tongue feature and tumor marker and baseline. The models' classification accuracies were 0.767 ± 0.081, 0.718 ± 0.062, and 0.688 ± 0.070, respectively, and the AUCs were 0.793 ± 0.086, 0.779 ± 0.075, and 0.771 ± 0.072, respectively. CONCLUSIONS: There were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. Due to the limited information, single data sources including baseline, tongue feature, and tumor marker cannot be used to identify the different stages of NSCLC in this pilot study. In addition to the logistic regression method, other machine learning methods, based on tumor marker and baseline data sets, can effectively improve the differential diagnosis efficiency of different stages of NSCLC by adding tongue image data, which requires further verification based on large sample studies in the future.

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation.

The rapid development of artificial intelligence technology has gradually extended from the general field to all walks of life, and intelligent tongue diagnosis is the product of a miraculous connection between this new discipline and traditional disciplines. We reviewed the deep learning methods and machine learning applied in tongue image analysis that have been studied in the last 5 years, focusing on tongue image calibration, detection, segmentation, and classification of diseases, syndromes, and symptoms/signs. Introducing technical evolutions or emerging technologies were applied in tongue image analysis; as we have noticed, attention mechanism, multiscale features, and prior knowledge were successfully applied in it, and we emphasized the value of combining deep learning with traditional methods. We also pointed out two major problems concerned with data set construction and the low reliability of performance evaluation that exist in this field based on the basic essence of tongue diagnosis in traditional Chinese medicine. Finally, a perspective on the future of intelligent tongue diagnosis was presented; we believe that the self-supervised method, multimodal information fusion, and the study of tongue pathology will have great research significance.

A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse.

Background and objective: Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. Methods: A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. Results: TB-L, TB-a, TB-b, TC-L, TC-a, h3, and h4/h1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. Conclusion: The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely.

Objective evaluation of tongue diagnosis ability using a tongue diagnosis e-learning/e-assessment system based on a standardized tongue image database.

Background: In Kampo medicine, tongue examination is used to diagnose the pathological condition "Sho," but an objective evaluation method for its diagnostic ability has not been established. We constructed a tongue diagnosis electronic learning and evaluation system based on a standardized tongue image database. Purpose: This study aims to verify the practicality of this assessment system by evaluating the tongue diagnosis ability of Kampo specialists (KSs), medical professionals, and students. Methods: In the first study, we analyzed the answer data of 15 KSs in an 80-question tongue diagnosis test that assesses eight aspects of tongue findings and evaluated the (i) test score, (ii) test difficulty and discrimination index, (iii) diagnostic consistency, and (iv) diagnostic match rate between KSs. In the second study, we administered a 20-question common Kampo test and analyzed the answer data of 107 medical professionals and 56 students that assessed the tongue color discrimination ability and evaluated the (v) correct answer rate, (vi) test difficulty, and (vii) factors related to the correct answer rate. Result: In the first study, the average test score was 62.2 ± 10.7 points. Twenty-eight questions were difficult (correct answer rate, <50%), 34 were moderate (50%-85%), and 18 were easy (≥85%). Regarding intrarater reliability, the average diagnostic match rate of five KSs involved in database construction was 0.66 ± 0.08, and as for interrater reliability, the diagnostic match rate between the 15 KSs was 0.52 (95% confidence interval, 0.38-0.65) for Gwet's agreement coefficient 1, and the degree of the match rate was moderate. In the second study, the difficulty level of questions was moderate, with a correct rate of 81.3% for medical professionals and 82.1% for students. The discrimination index was good for medical professionals (0.35) and poor for students (0.06). Among medical professionals, the correct answer group of this question had a significantly higher total score on the Kampo common test than the incorrect answer group (85.3 ± 8.4 points vs. 75.8 ± 11.8 points, p < 0.01). Conclusion: This system can objectively evaluate tongue diagnosis ability and has high practicality. Utilizing this system can be expected to contribute to improving learners' tongue diagnosis ability and standardization of tongue diagnosis.

[Discussion on Technical Evaluation of Tongue Diagnosis Equipment of Traditional Chinese Medicine].

This study briefly introduces the tongue diagnostic equipment of traditional Chinese medicine. It analyzes and discusses the key points of technical evaluation of tongue diagnostic equipment from the aspects of product name, performance parameters, image processing functions, product use methods, clinical evaluation, etc. It analyzes the safety risks and effectiveness indicators of tongue diagnostic equipment, hoping to bring some help to the gradual standardization of tongue diagnostic equipment and the registration of enterprises.

Tongue diagnosis index of chronic kidney disease.

BACKGROUND: To apply non-invasive Automatic Tongue Diagnosis System (ATDS) in analyzing tongue features in patients with chronic kidney disease (CKD). METHODS: This was a cross-sectional, case-controlled observational study. Patients with CKD who met the inclusion and exclusion criteria were enrolled and divided into the following groups according to renal function and dialysis status: non-dialysis CKD group; end-stage renal disease (ESRD) group; and control group. Tongue images were captured and eight tongue features-shape, color, fur thickness, saliva, fissure, ecchymosis, teeth marks, and red dots-were imaged and analyzed by ATDS. RESULTS: 117 participants (57 men, 60 women) were enrolled in the study, which included 16 in control group, 38 in non-dialysis CKD group, and 63 in ESRD group. We demonstrated significant differences in the fur thickness (p = 0.045), color (p = 0.005), amounts of ecchymosis (p = 0.010), teeth marks (p = 0.016), and red dot (p < 0.001) among three groups. The areas under receiver operating characteristic curve for the amount of ecchymosis was 0.757 ± 0.055 (95% confidence interval, 0.648-0866; p < 0.001). Additionally, with increase in ecchymosis by one point, the risk of CKD dialysis rose by 1.523 times (95% confidence interval, 1.198-1.936; p = 0.001). After hemodialysis, the amount of saliva (p = 0.038), the area of saliva (p = 0.048) and the number of red dots (p = 0.040) were decreased significantly among patients with ESRD. On the contrary, the percentage of coating (p = 0.002) and area of coating (p = 0.026) were increased significantly after hemodialysis. CONCLUSION: Blood deficiency and stasis with qi deficiency or blood heat syndrome (Zheng pattern) is common in patients with CKD. The risk of CKD dialysis increases with increasing ecchymosis. Hemodialysis can affect saliva, tongue coating, and relieve heat syndrome among ESRD patients.

Discussion on Technical Evaluation of Tongue Diagnosis Equipment of Traditional Chinese Medicine / 中国医疗器械杂志

This study briefly introduces the tongue diagnostic equipment of traditional Chinese medicine. It analyzes and discusses the key points of technical evaluation of tongue diagnostic equipment from the aspects of product name, performance parameters, image processing functions, product use methods, clinical evaluation, etc. It analyzes the safety risks and effectiveness indicators of tongue diagnostic equipment, hoping to bring some help to the gradual standardization of tongue diagnostic equipment and the registration of enterprises.

Objective diagnosis of tongue in Kampo Medicine / 全日本鍼灸学会雑誌

Tongue diagnosis in Kampo medicine is considered to be a diagnostic method that can provide information about a patient's constitution and medical condition. We have identified the following problems in tongue diagnosis: the influence of external environmental factors such as light source, room temperature, and dryness, as well as subjective factors that depend on the knowledge and experience of medical doctors. To overcome these problems and to support Kampo diagnosis, we developed the Tongue Image Analyzing System (TIAS). Regarding color, objective numerical values L*a*b* were measured and standardization and objectification were promoted. We introduce some of the progress that has been made over the past 15 years since the development of TIAS.

Literature review and prospect on oral cognition and disease diagnosis and treatment between Han and Tang dynasties / 华西口腔医学杂志

Chinese medicine entered a significant period from foundation to maturity between Han and Tang dynasties when the Chinese traditional stomatology was a key stage. Sorting and analysis of existing literature and research outcomes have showed that current research on stomatology between Han and Tang dynasties focuses on oral physiology, pathology, diagnosis and treatment, and health care. It also involves stomatology history and explanation of termino-logies related to mouth and teeth recorded in medical books, use of simple methods, and thinking with citation and analysis of literature simply listed and reasoning preliminarily deducted. From the macro perspective, current research has not unveiled the whole picture of stomatology between the two dynasties and left a series of key issues unresolved. Thus, new methods should be developed and employed to carry out medical research on stomatology between Han and Tang dynasties given that is has a prosperous future.