The tongue features associated with type 2 diabetes mellitus

Association between color value of tongue and T2DM based on dose-response analyses using restricted cubic splines in China: A cross-sectional study

This study aimed to explore the relationship between international commission on illumination (CIE) L*a*b* color value of tongue and type 2 diabetes mellitus (T2DM). We used restricted cubic spline method and logistic regression method to assess the relationship between CIE L*a*b* color value of tongue and T2DM. A total of 2439 participants (991 T2DM and 1448 healthy) were included. A questionnaire survey and tongue images obtained with tongue diagnosis analysis-1 were analyzed. As required, chi-square and t tests were applied to compare the T2DM and healthy categories. Our findings suggest the 95% confidence interval and odds ratio for body mass index, hypertension, and age were 0.670 (0.531-0.845), 13.461 (10.663-16.993), and 2.595 (2.324-2.897), respectively, when compared to the healthy group. A linear dose-response relationship with an inverse U-shape was determined between CIE L* and CIE a* values and T2DM (P < .001 for overall and P < .001 for nonlinear). Furthermore, U-shaped and linear dose-response associations were identified between T2DM and CIE b* values (P = .0160 for nonlinear). Additionally, in adults, the CIE L*a*b* color value had a correlation with T2DM. This novel perspective provides a multidimensional understanding of traditional Chinese medicine tongue color, elucidating the potential of CIE L*a*b* color values of tongue in the diagnosis of T2DM.

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.

Distinct microbiome of tongue coating and gut in type 2 diabetes with yellow tongue coating

The gut microbiome plays a critical role in the pathogenesis of type 2 diabetes mellitus (T2DM). However, the inconvenience of obtaining fecal samples hinders the clinical application of gut microbiome analysis. In this study, we hypothesized that tongue coating color is associated with the severity of T2DM. Therefore, we aimed to compare tongue coating, gut microbiomes, and various clinical parameters between patients with T2DM with yellow (YC) and non-yellow tongue coatings (NYC). Tongue coating and gut microbiomes of 27 patients with T2DM (13 with YC and 14 with NYC) were analyzed using 16S rDNA gene sequencing technology. Additionally, we measured glycated hemoglobin (HbA1c), random blood glucose (RBG), fasting blood glucose (FBG), postprandial blood glucose (PBG), insulin (INS), glucagon (GC), body mass index (BMI), and homeostasis model assessment of β-cell function (HOMA-β) levels for each patient. The correlation between tongue coating and the gut microbiomes was also analyzed. Our findings provide evidence that the levels of Lactobacillus spp. are significantly higher in both the tongue coating and the gut microbiomes of patients with YC. Additionally, we observed that elevated INS and GC levels, along with decreased BMI and HOMA-β levels, were indicative of a more severe condition in patients with T2DM with YC. Moreover, our results suggest that the composition of the tongue coating may reflect the presence of Lactobacillus spp. in the gut. These results provide insights regarding the potential relationship between tongue coating color, the gut microbiome, and T2DM.

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.

Mirror-like tongue is an important predictor of acute heart failure: a cohort study of acute heart failure in Chinese patients

OBJECTIVE

To reveal that mirror-like tongue, observed via a noninvasive inspection, is a powerful indicator of the severity and prognosis of patients with acute heart failure (AHF).

METHODS

This was an observational, prospective study. A total of 408 patients who met the inclusion criteria and were diagnosed with AHF for the first time at Taicang Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine from August 2019 to January 2021 were selected as the research subjects. According to their tongue fur, the patients were divided into group A (mirror-like tongue group) and group B (non-mirror-like tongue group). The clinical characteristics and incidence of major adverse cardiovascular events (MACEs) within 1 year of follow-up were systematically compared between the two groups.

RESULTS

Sixty-five patients were included in group A, and 343 patients were included in group B. There were 32 males and 33 females in group A and 168 males and 175 females in group B. The average age of the overall population was 64 years old, and the average age of group A was significantly higher than that of group B (74 vs 62 years, P < 0.001). Compared with that in group B, the left ventricular ejection fraction (LVEF) in group A was significantly lower (35% vs 42%, P < 0.001), and the log N-terminal pro B-type natriuretic peptide (NT-proBNP) level was significantly higher (4.0 vs 3.4, P < 0.001). The proportion of the combined application of vasoactive drugs in group A was significantly higher than that in group B (64% vs 38%, P < 0.001). Group B had a higher proportion of coronary angiography (29.5% vs 16.9%, P = 0.038). Group A was more inclined to require mechanical ventilation than group B (33.9% vs 22.5%, P = 0.049). The length of hospital stay in group A was significantly longer than that in group B (13.1 vs 7.6, P < 0.001). The incidence of MACEs, such as recurrence of AHF, new myocardial infarction and stroke, in group A within one year was higher than that in group B (P = 0.007, 0.009, < 0.001). The incidence of cumulative MACEs in group A was significantly higher than that in group B [hazard ratio = 2.76, 95% confidence interval (1.73, 4.41), P < 0.001]. Univariate and multivariate Cox regression analyses showed that mirror-like tongue, age, length of stay, LVEF and log NT-proBNP were independent predictors of MACEs in patients with AHF within one year.

CONCLUSIONS

Noninvasive tongue inspection technology can be used as a powerful tool for assessing the severity of illness and predicting prognosis in patients with AHF. A mirror-like tongue is an independent risk factor for MACEs in patients with AHF during the first year and has a combination effect with age, length of hospital stay, ejection fraction and NT-proBNP on the occurrence of MACEs.

Oral Manifestations: A Reliable Indicator for Undiagnosed Diabetes Mellitus Patients

OBJECTIVES

 This article identifies undiagnosed DM (UDM) cases in the Pakistani population by perceiving the signs and symptoms of DM and associating them with oral manifestations.

MATERIAL AND METHODS

 In this cross-sectional study, patients showing at least three or more classical or warning signs like polydipsia, polyuria, polyphagia, and general weakness were considered UDM cases. Detailed oral examination for gingivitis, periodontitis, halitosis, xerostomia, and tongue manifestations was done followed by the hemoglobin A1c (HbA1c) analysis.

RESULTS

 Out of 5,878 patients, 214 UDM cases were identified, where 31.8% and 39.7% of the patients were diagnosed as prediabetics and diabetics, respectively, based on HbA1c analysis. Prevalence of gingivitis (97.6%), fissured tongue (91.8%), generalized periodontitis (85.9%), thick saliva (87.1%), xerostomia (84.7%), burning mouth syndrome (63.5%), yellow discoloration of tongue (57.6%), and ecchymosis/ulcers (43.5%) were more in diabetics as compared to prediabetic patients and normal population.

CONCLUSION

 The oral manifestations can be crucial for identifying UDM cases. Dentists can play a pivotal role by taking detailed history and thorough oral examination. If three or more symptoms as concluded above are present, an HbA1c analysis should be conducted to prevent preop and postop complications associated with DM.

Predictive value of TCM clinical index for diabetic peripheral neuropathy among the type 2 diabetes mellitus population: A new observation and insight

Aims

The objectives of this study were to identify clinical predictors of the Traditional Chinese medicine (TCM) clinical index for diabetic peripheral neuropathy (DPN) in type 2 diabetes mellitus (T2DM) patients, develop a clinical prediction model, and construct a nomogram.

Methods

We collected the TCM clinical index from 3590 T2DM recruited at the Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine from January 2019 to October 2020. The participants were randomly assigned to either the training group (n = 3297) or the validation group (n = 1426). TCM symptoms and tongue characteristics were used to assess the risk of developing DPN in T2DM patients. Through 5-fold cross-validation in the training group, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection. In addition, using multifactor logistic regression analysis, a predictive model and nomogram were developed.

Results

A total of eight independent predictors were found to be associated with the DPN in multivariate logistic regression analyses: advanced age of grading (odds ratio/OR 1.575), smoke (OR 2.815), insomnia (OR 0.557), sweating (OR 0.535), loose teeth (OR 1.713), dry skin (OR 1.831), purple tongue (OR 2.278). And dark red tongue (OR 0.139). The model was constructed using these eight predictor's medium discriminative capabilities. The area under the curve (AUC) of the training set is 0.727, and the AUC of the validation set is 0.744 on the ROC curve. The calibration plot revealed that the model's goodness-of-fit is satisfactory.

Conclusions

We established a TCM prediction model for DPN in patients with T2DM based on the TCM clinical index.

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.

A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

Gastric cancer has become a global health issue, severely disrupting daily life. Early detection in gastric cancer patients and immediate treatment contribute significantly to the protection of human health. However, routine gastric cancer examinations carry the risk of complications and are time-consuming. We proposed a framework to predict gastric cancer non-invasively and conveniently. A total of 703 tongue images were acquired using a bespoke tongue image capture instrument, then a dataset containing subjects with and without gastric cancer was created. As the images acquired by this instrument contain non-tongue areas, the Deeplabv3+ network was applied for tongue segmentation to reduce the interference in feature extraction. Nine tongue features were extracted, relationships between tongue features and gastric cancer were explored by using statistical methods and deep learning, finally a prediction framework for gastric cancer was designed. The experimental results showed that the proposed framework had a strong detection ability, with an accuracy of 93.6%. The gastric cancer prediction framework created by combining statistical methods and deep learning proposes a scheme for exploring the relationships between gastric cancer and tongue features. This framework contributes to the effective early diagnosis of patients with gastric cancer.

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-β (TGF-β) than non-GLM controls (P < .05). Also, tongue coating contrast and entropy values were significantly correlated with WBC or TGF-β 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.

Machine Learning-Based Technique for the Severity Classification of Sublingual Varices according to Traditional Chinese Medicine

Tongue diagnosis, a noninvasive examination, is an essential step for syndrome differentiation and treatment in traditional Chinese medicine (TCM). Sublingual vein (SV) is examined to determine the presence of blood stasis and blood stasis syndrome. Many studies have shown that the degree of SV stasis positively correlates with disease severity. However, the diagnoses of SV examination are often subjective because they are influenced by factors such as physicians’ experience and color perception, resulting in different interpretations. Therefore, objective and scientific diagnostic approaches are required to determine the severity of sublingual varices. This study aims at developing a computer-assisted system based on machine learning (ML) techniques for diagnosing the severity of sublingual varicose veins. We conducted a comparative study of the performance of several supervised ML models, including the support vendor machine, K-neighbor, decision tree, linear regression, and Ridge classifier and their variants. The main task was to differentiate sublingual varices into mild and severe by using images of patients’ SVs. To improve diagnostic accuracy and to accelerate the training process, we proposed using two model reduction techniques, namely, the principal component analysis in conjunction with the slice inverse regression and the convolution neural network (CNN), to extract valuable features during the preprocessing of data. Our results showed that these two extraction methods can reduce the training time for the ML methods, and the Ridge-CNN method can achieve an accuracy rate as high as 87.5%, which is similar to that of experienced TCM physicians. This computer-aided tool can be used for reference clinical diagnosis. Furthermore, it can be employed by junior physicians to learn and to use in clinical settings.

Tongue Manifestation in Patients with Osteonecrosis of the Femoral Head: A Cross-sectional Study

OBJECTIVE

Although tongue manifestation is a vital component of Traditional Chinese Medicine (TCM), relevant research on patients with osteonecrosis of the femoral head (ONFH) is still lacking. This study will explore the characteristic tongue manifestation of ONFH patients to inform future research and clinical practice.

METHODS

This is a cross-sectional study. All ONFH patients meeting criteria and their clinical data were collected from the online China osteonecrosis of the femoral head database (CONFHD) since it was created. Organized tongue manifestations of eligible patients through the tongue manifestation acquisition instrument, including tongue shape, tongue color, tongue coating thickness, tongue coating color and tongue coating moisture. We used descriptive analysis for the general information while systematic clustering analysis for the better summary of tongue characteristics.

RESULTS

A total of 375 ONFH patients were included with an average age of 46.3 years. Most patients appeared with enlarged tongue body (54.4%), and the proportions of pale and red tongue (62.4%) were higher than others. Tongue coating were mainly showed as thick (64.5%), white (57.6%) and moist (79.7%). Comparison of tongue shape between different causes of ONFH had a significant statistically difference (P = 0.000). Tongue manifestations could be cluster analyzed into three categories which were matched into four TCM syndromes.

CONCLUSIONS

The tongue manifestation of ONFH patients has a significant change both in tongue body and coating, and different features may be related to the ONFH pathology. This study provides new and valuable tongue informations for a preliminary screening of ONFH patients.

Exploring the pivotal variables of tongue diagnosis between patients with acute ischemic stroke and health participants

Background and aim

Stroke is a major cause of disability worldwide, and ischemic stroke is the most common type of stroke. The prevention and treatment of ischemic stroke remain a challenge worldwide. Traditional Chinese medicine (TCM) is often sought to provide an alternative therapy for the prevention and rehabilitation intervention of ischemic stroke in Taiwan. Therefore, this study explored the pivotal variables of tongue diagnosis among acute ischemic stroke and healthy participants in middle and older age.

Experimental procedure

This was a cross-sectional and case-controlled study. Data were collected from 99 patients with acute ischemic stroke and 286 healthy participants who received tongue diagnoses at Changhua Christian Hospital (CCH) from September 1, 2014, to December 31, 2016. Tongue features were extracted using the automatic tongue diagnosis system. Nine tongue features, including tongue shape, tongue color, fur thickness, fur color, saliva, tongue fissures, ecchymoses, teeth marks, and red spots were analyzed.

Results and conclusion

Objective image analysis techniques were used to identify significant differences in the many tongue features between patients with acute ischemic stroke and individuals without stroke. According to the logistic regression analysis, pale tongue color (OR:5.501, p = 0.001), bluish tongue color (OR:4.249, p = 0.014), ecchymoses (OR:1.058, p < 0.001), and tongue deviation angle (OR:1.218, p < 0.001) were associated with significantly increased odds ratios for acute ischemic stroke. The research revealed that tongue feature abnormalities were significantly related to the occurrence of ischemic stroke.

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TCM provides a complementary therapy for stroke.

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ATDS serves as a non-invasive, objective, and reliable tool in TCM.

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A significantly higher prevalence of abnormal tongue features in acute ischemic stroke.

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Tongue diagnosis could serve as a feasible predictor of stroke.

Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation

ETHNOPHARMACOLOGICAL RELEVANCE

Tongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms based on TCM theory.

AIM OF THE STUDY

The present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19.

MATERIALS AND METHODS

Herein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doctors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19.

RESULTS

The overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and accuracy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors' examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet.

CONCLUSIONS

Our framework may provide an important research paradigm for differentiating tongue characteristics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.

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.

A Perspective on Tongue Diagnosis in Patients with Breast Cancer

Introduction

Breast cancer (BC) is the most common cancer in women and patients with BC often undergo complex treatment. In Taiwan, nearly 80% of patients with BC seek traditional Chinese medicine (TCM) during adjuvant chemotherapy to relieve discomfort and side effects. This study investigated tongue features and pattern differentiation through noninvasive TCM tongue diagnosis in patients with BC.

Materials and Methods

This cross-sectional, case-controlled, retrospective observational study collected patient data through a chart review. The tongue features were extracted using the automatic tongue diagnosis system (ATDS). Nine tongue features, including tongue shape, tongue color, fur thickness, fur color, saliva, tongue fissures, ecchymoses, teeth marks, and red dots, were analyzed. Results and Discussion. Objective image analysis techniques were used to identify significant differences in the many tongue features between BC patients and non-BC individuals. A significantly larger proportion of patients with BC had a small tongue (p < 0.001), pale tongue (p < 0.001), thick fur (p < 0.001), yellow fur (p < 0.001), wet saliva (p < 0.001), thick tongue fur (p < 0.001), fissures (p=0.040), and ecchymoses in the heart-lung area (p=0.013). According to logistic regression, small tongue shape, pale tongue color, yellow fur color, wet saliva, and the amounts of fissures were associated with a significantly increased odds ratio for BC.

Conclusions

This study showed significant differences in tongue features, such as small tongue shape, pale tongue color, thick fur, yellow fur color, wet saliva, fissure, and ecchymoses in the heart-lung area in patients with BC. These tongue features would imply yin deficiency, deficiencies of blood, stagnation of heat, and phlegm/blood stasis in TCM theory. There is a need to investigate effective and safe treatment to enhance the role of TCM in integrated medical care for patients with BC.

A Nonlinear Association between Tongue Fur Thickness and Tumor Marker Abnormality: A Cross-Sectional Study

Background

Many associations between tongue fur and different physiological and biochemical indexes have been revealed. However, the relationship between tongue fur and tumor markers remains unexplored.

Methods

We collected the medical examination reports of 1625 participants. Participants were residents of Chengdu, China, undergoing routine health checkups at the health management center of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine between December 2018 and September 2020. The participants' tongue fur thickness was measured using the DAOSH four-diagnostic instrument. Tumor marker levels, including t-PSA, AFP, CEA, CA125, and CA199, were measured in the clinical laboratory. Curve-fitting and multivariable logistic regression were used to analyze the association between tongue fur thickness and tumor marker abnormality.

Results

Curve-fitting showed that the relationship between tongue fur thickness and abnormal tumor marker rate was nonlinear, similar to a U shape. As the tongue fur thickness value increased, the abnormal tumor marker probability initially decreased and then increased. Logistic regression showed that, in the crude model, compared with the thin tongue fur group, the odds ratios (ORs) and 95% confidence intervals (CIs) of the less or peeling tongue fur group and thick tongue fur group for tumor marker abnormality were 1.79 (1.02–3.17) and 1.70 (1.13–2.54), respectively. After adjusting gender, age, body mass index (BMI), smoking history, drinking history, tongue color, the form of the tongue, and fur color, the ORs and 95% CIs of the less or peeling tongue fur group and thick tongue fur group were 1.93 (1.04–3.57) and 1.82 (1.17–2.81), respectively.

Conclusions

Excessive or very little tongue fur is associated with tumor marker abnormality. Further cross-sectional studies are needed to evaluate the clinical value of tongue fur for cancer diagnosis and screening.

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.

Tongue cleaning in the elderly and its role in the respiratory and swallowing functions: Benefits and medical perspectives

Oral dysfunction, including oral uncleanness and decline in tongue motor function, tongue pressure and swallowing function, precedes frailty. The tongue's dorsum is a reservoir of oral microbiota, desquamated epithelial mucosa and leukocytes due to the multi-papillate anatomy, and leads to tongue coating. The tongue coating is frequently found in older adults because of hyposalivation, immunity's hypoactivity, diminished motor function and compromised tongue's pressure with age. Anaerobe-driven volatile sulphur compounds in tongue coating are a major cause of intra-oral malodor. Dysbiosis of the tongue-coating microbiome rather than the amount of microorganisms is associated with a risk of aspiration pneumonia. Daily tongue cleaning with a brush or scraper is an easy way to control tongue coating deposits and quality. Using mouth wash or rinse-containing germicides is also a way to control the microbiota of tongue coating. The tongue function is closely related to swallowing. Tongue and suprahyoid muscles are linked with respiratory muscles through the endothoracic fascia. The mechanical stimulation during the cleaning of the tongue may stimulate the respiratory muscles. An intervention trial revealed that tongue cleaning by mucosal brush improves tongue pressure, swallowing and respiratory function in old residents of nursing homes, suggesting a rehabilitative effect of tongue cleaning on the swallowing and respiratory functions, preventing aspiration pneumonia. This narrative review assesses the tongue-cleaning benefits for respiratory and swallowing functions and the possibility of preventing aspiration pneumonia.

Tongue-coating microbiome as a cancer predictor: A scoping review

OBJECTIVE

The tongue microbiome has emerged as a non-invasive diagnostic and tracking prognostic tool in the detection of diseases mainly cancer. This scoping review aimed to identify the association between tongue microbiome and pre-cancer or cancer lesions.

DESIGN

A comprehensive electronic database search including PubMed, Web of Science, and Scopus was undertaken up to March 2021, without language or date restrictions. This review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. All observational studies that compared microbial community on the dorsal surface of the tongue between cancer or precancerous cases and healthy controls using NGS techniques were included.

RESULTS

Of 274 records identified, nine studies were eligible to be included. Despite the inconsistent observations in terms of diversity and richness, most studies reported alteration in bacterial communities between pre-cancer or cancer cases and control groups. The bacterial profile among cases was so far correlated at the phylum level with a noticeable diverse degree at the genus level. The majority of included studies reported a higher abundance of certain kinds of microorganisms as compared to healthy participants including Firmicutes, Fusobacteria and Actinobacteria at phyla level as well as Streptococcus, Actinomyces, Leptotrichia, Campylobacter, and Fusobacterium at the genus level.

CONCLUSION

The alteration of the tongue microbial community has been associated with several diseases mainly cancer. So, the tongue microbiome may serve as a promising diagnostic tool or as a long-term monitor in precancerous or cancer cases.

Oral, Tongue-Coating Microbiota, and Metabolic Disorders: A Novel Area of Interactive Research

Interactions between colonizing microbiota and the host have been fully confirmed, among which the tongue-coating microbiota have a moderate rate of renewal and disease sensitivity and are easily obtained, making them an ideal research subject. Oral microbiota disorders are related to diabetes, obesity, cardiovascular disease, cancer, and other systemic diseases. As an important part of the oral cavity, tongue-coating microbiota can promote gastritis and digestive system tumors, affecting the occurrence and development of multiple chronic diseases. Common risk factors include diet, age, and immune status, among others. Metabolic regulatory mechanisms may be similar between the tongue and gut microbiota. Tongue-coating microbiota can be transferred to the respiratory or digestive tract and create a new balance with local microorganisms, together with the host epithelial cells forming a biological barrier. This barrier is involved in the production and circulation of nitric oxide (NO) and the function of taste receptors, forming the oral-gut-brain axis (similar to the gut-brain axis). At present, the disease model and mechanism of tongue-coating microbiota affecting metabolism have not been widely studied, but they have tremendous potential.

Computational Traditional Chinese Medicine diagnosis: A literature survey

BACKGROUND AND OBJECTIVE

Traditional Chinese Medicine (TCM) diagnosis is based on the theoretical principles and knowledge, where it is steeped in thousands of years of history to diagnose various types of diseases and syndromes. It can be generally divided into four main diagnostic approaches: 1. Inspection, 2. Auscultation and olfaction, 3. Inquiry, and 4. Palpation, which are widely used in TCM hospitals in China and around the world. With the development of intelligent computing technology in recent years, computational TCM diagnosis has grown rapidly.

METHODS

In this paper, we aim to systematically summarize the development of computational TCM diagnosis based on four diagnostic approaches, mainly focusing on digital acquisition devices, collected datasets, and computational detection approaches (algorithms). Furthermore, all related works of this field are compared and explored in detail.

RESULTS

This survey provides the principles, applications, and current progress in computing for readers and researchers in terms of computational TCM diagnosis. Moreover, the future development direction, prospect, and technological trend of computational TCM diagnosis will also be discussed in this study.

CONCLUSIONS

Recent computational TCM diagnosis works are compared in detail to show the pros/cons, where we provide some meaningful suggestions and opinions on the future research approaches in this area. This work is useful for disease detection in computational TCM diagnosis as well as health management in the smart healthcare area.

INDEX TERMS

Computational diagnosis, Traditional Chinese Medicine, survey, smart healthcare.

The tongue features associated with chronic kidney disease

BACKGROUND

Traditional Chinese medicine (TCM) tongue diagnosis plays an important role in differentiation of symptoms because the tongue reflects the physiological and pathological condition of the body. The automatic tongue diagnosis system (ATDS), which noninvasively captures tongue images, can provide objective and reliable diagnostic information. Chronic kidney disease (CKD) currently is an important global public health problem and contributor to morbidity and mortality from non-communicable diseases. Thus, it is interesting to analyze and probe the relationship between tongue examination and CKD.

METHODS

This protocol is a cross-sectional, case-controlled observational study investigating the usefulness of the ATDS in clinical practice by examining its efficacy as a diagnostic tool for CKD. Volunteers over 20 years old with and without CKD will be enrolled. Tongue images will be captured and the patients divided into 2 groups: CKD group and healthy group. Nine primary tongue features will be extracted and analyzed, including tongue shape, tongue color, tooth mark, tongue fissure, fur color, fur thickness, saliva, ecchymosis, and red dots.

RESULT

The results of this study will systematically evaluate tongue manifestations of patients and examine its efficacy as an early detection and diagnosis of CKD.

DISCUSSION

The aim of this protocol is to investigate discriminating tongue features to distinguish between CKD and normal people, and establish differentiating index to facilitate the noninvasive detection of CKD.

TRIAL REGISTRIES

ClinicalTrials.gov; Identifier: NCT04708743.

Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques

BACKGROUND

Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contribute to serious multisystem complications, which can be life threatening. Timely detection and intervention of prediabetes is very important to prevent diabetes, because it is inevitable in the development and progress of the disease.

OBJECTIVE

Our objective was to establish the predictive model that can be applied to evaluate people with blood glucose in high and critical state.

METHODS

We established the diabetes risk prediction model formed by a combined TCM tongue diagnosis with machine learning techniques. 1512 subjects were recruited from the hospital. After data preprocessing, we got the dataset 1 and dataset 2. Dataset 1 was used to train classical machine learning model, while dataset 2 was used to train deep learning model. To evaluate the performance of the prediction model, we used Classification Accuracy(CA), Precision, Recall, F1-score, Precision-Recall curve(P-R curve), Area Under the Precision-Recall curve(AUPRC), Receiver Operating Characteristic curve(ROC curve), Area Under the Receiver Operating Characteristic curve(AUROC), then selected the best diabetes risk prediction model.

RESULTS

On the test set of dataset 1, the CA of non-invasive Stacking model was 71 %, micro average AUROC was 0.87, macro average AUROC was 0.84, and micro average AUPRC was 0.77. In the critical blood glucose group, the AUROC was 0.84, AUPRC was 0.67. In the high blood glucose group, AUROC was 0.87, AUPRC was 0.83. On the validation set of dataset 2, the CA of ResNet50 model was 69 %, micro average AUROC was 0.84, macro average AUROC was 0.83, and micro average AUPRC was 0.73. In the critical blood glucose group, AUROC was 0.88, AUPRC was 0.71. In the high blood glucose group, AUROC was 0.80, AUPRC was 0.76. On the test set of dataset 2, the CA of ResNet50 model was 65 %, micro average AUROC was 0.83, macro average AUROC was 0.82, and micro average AUPRC was 0.71. In the critical blood glucose group, the prediction of AUROC was 0.84, AUPRC was 0.60. In the high blood glucose group, AUROC was 0.87, AUPRC was 0.71.

CONCLUSIONS

Tongue features can improve the prediction accuracy of the diabetes risk prediction model formed by classical machine learning model significantly. In addition to the excellent performance, Stacking model and ResNet50 model which were recommended had non-invasive operation and were easy to use. Stacking model and ResNet50 model had high precision, low false positive rate and low misdiagnosis rate on detecting hyperglycemia. While on detecting blood glucose value in critical state, Stacking model and ResNet50 model had a high sensitivity, a low false negative rate and a low missed diagnosis rate. The study had proved that the differential changes of tongue features reflected the abnormal glucose metabolism, thus the diabetes risk prediction model formed by a combined TCM tongue diagnosis and machine learning technique was feasible.

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.

Oral Health Messiers: Diabetes Mellitus Relevance

This article aims to narrate the various oral complications in individuals suffering from diabetes mellitus. Google search for "diabetes mellitus and oral complications" was done. The search was also carried out for "diabetes mellitus" and its oral complications individually. Diabetes mellitus is a chronic metabolic disorder that is a global epidemic and a common cause of morbidity and mortality in the world today. Currently, there are about 422 million cases of diabetes mellitus worldwide. Diabetic patients can develop different complications in the body such as retinopathy, neuropathy, nephropathy, cardiovascular disease. Complications in the oral cavity have been observed in individuals suffering from diabetes mellitus. A study noted that more than 90% of diabetic patients suffered from oral complications. Another research has shown a greater prevalence of oral mucosal disorders in patients with diabetes mellitus than non-diabetic population: 45-88% in patients with type 2 diabetes compared to 38.3-45% in non-diabetic subjects and 44.7% in type 1 diabetic individuals compared to 25% in the non-diabetic population. Oral complications in people with diabetes are periodontal disease, dental caries, oral infections, salivary dysfunction, taste dysfunction, delayed wound healing, tongue abnormalities, halitosis, and lichen planus. The high glucose level in saliva, poor neutrophil function, neuropathy, and small vessel damage contribute to oral complications in individuals with uncontrolled diabetes. Good oral health is imperative for healthy living. Oral complications cause deterioration to the quality of life in diabetic patients. Complications like periodontal disease having a bidirectional relationship with diabetes mellitus even contribute to increased blood glucose levels in people with diabetes. This article intends to promote awareness regarding the oral health of diabetics and to stress the importance of maintaining proper oral hygiene, taking preventive measures, early detection, and appropriate management of oral complications of these patients through a multidisciplinary approach.

Variations of Oral Microbiome in Chronic Insomnia Patients with Different Tongue Features

Chronic insomnia is a disease which brings intense mental pain and disturbing complications to patients worldwide. The oral microbiome exhibits a mechanistic influence on human health. Therefore, it is crucial to understand the oral microbial diversity in insomnia. Tongue diagnosis has been considered a critical basic procedure in insomnia therapeutic decision-making in Traditional Chinese Medicine (TCM). Hence, it is significant to elucidate the various oral microbiome differences in chronic insomnia patients with different tongue features. In this paper, we used 16S rRNA gene sequencing and bioinformatics analysis to investigate dynamic changes in oral bacterial profile and correlations between chronic insomnia patients and healthy individuals, as well as in patients with different tongue coatings. Moreover, the relationship between the severity of insomnia and oral microbiota was explored. Our findings showed that chronic insomnia patients harbored a significantly higher diversity of oral bacteria when compared to healthy controls. More importantly, the results revealed that the diversity and relative abundance of the bacterial community was significantly altered among different tongue coatings in patients but not in healthy individuals. Oral bacteria with a relative abundance [Formula: see text]1% and [Formula: see text] among different tongue groups were considered remarkable bacteria, which included three phyla Proteobacteria, Bacteroidetes, Gracilibacteria, and four genera, Streptococcus, Prevotella_7, Rothia, and Neisseria. Our findings indicate that changes in oral microbiome correlate with tongue coatings in patients with chronic insomnia. Thus, the remarkable microbiome may provide inspiration for further studies on the correlation between tongue diagnosis and oral microbiome in chronic insomnia patients.

Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark

Tongue diagnosis plays a pivotal role in traditional Chinese medicine (TCM) for thousands of years. As one of the most important tongue characteristics, tooth-marked tongue is related to spleen deficiency and can greatly contribute to the symptoms differentiation and treatment selection. Yet, the tooth-marked tongue recognition for TCM practitioners is subjective and challenging. Most of the previous studies have concentrated on subjectively selected features of the tooth-marked region and gained accuracy under 80%. In the present study, we proposed an artificial intelligence framework using deep convolutional neural network (CNN) for the recognition of tooth-marked tongue. First, we constructed relatively large datasets with 1548 tongue images captured by different equipments. Then, we used ResNet34 CNN architecture to extract features and perform classifications. The overall accuracy of the models was over 90%. Interestingly, the models can be successfully generalized to images captured by other devices with different illuminations. The good effectiveness and generalization of our framework may provide objective and convenient computer-aided tongue diagnostic method on tracking disease progression and evaluating pharmacological effect from a informatics perspective.

Gender- and age-dependent tongue features in a community-based population

This study, an important groundwork for clinical tongue diagnosis and future traditional Chinese medicine (TCM) research, tested the hypothesis that some tongue features vary significantly between different gender and age groups by utilizing an automatic tongue diagnosis system (ATDS).A cross-sectional study of 1487 participants from a community-based population was performed. Study subjects with ages ranging from 20 to 92 were categorized into 3 groups: <40, 40 to 64, and ≥65 years old, and the subjects were also stratified according to gender. Tongue images were collected at the end of each normal health examination routine to further derive the relevant tongue features of every participant by using the ATDS developed by our team. There were a total of nine tongue features that were identified: tongue shape, tongue color, fur thickness, fur color, saliva, tongue fissure, ecchymosis, teeth mark, and red dot. The corresponding tongue features, demography, and physical/laboratory examination data were compared between different gender and age groups.Our study showed that, compared to females, males had enlarged tongue shape, thicker fur, more fissures and fewer teeth marks (all P < .001), and also had more red tongue color (P = .019), normal saliva (P = .001), more red dots (P = .005) and yellower fur (P = .014). In females, increasing age was associated with more enlarged tongue shape, thicker fur, yellower fur, more saliva, fissures and fewer teeth marks (all P < .001), more ecchymoses (P = .009), and more red tongue color (P = .023). These associations of age with more fissures, fewer teeth marks, fewer red dots (P < .001), median tongue shape (P = .029), and wet saliva (P = .014) were also evident in males, but other relationships were not clearly evident.Even though most of the common tongue features derived from a community-based population are consistent with TCM theory, yet some significantly gender- and age-dependent tongue characteristics were identified. These disparities in tongue features associated with gender or age shall be prudently taken into consideration in clinical tongue diagnosis and future TCM research.

Research progress on Traditional Chinese Medicine syndromes of diabetes mellitus

With the improvement of people's living standard and the changes of environment, the incidence of diabetes mellitus (DM) is on the rise day by day, while clinical treatment mainly aims at lowering blood glucose, instead of fundamental prevention and treatment. What's worse, the measures of prevention and treatment of DM complications remain inadequate. Both Chinese and modern medicine have advantages and disadvantages in treating DM, therefore, it would be a worthy attempt to break through the bottleneck of DM treatment by combining the advantages of both, and explore the new measures to prevent and deal with DM from the perspective of the combination of Traditional Chinese Medicine (TCM) syndrome and modern medicine. In this paper, modern research methods and possible indicators of TCM syndromes of DM were expounded from clinical and basic research aspects, aiming to find specific biomarkers of TCM syndromes, and providing experimental supports for the diagnosis and treatment of DM and the verification of TCM theory.