Breast cancer index: a perspective on tongue diagnosis in traditional chinese medicine

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.

Missing-view completion for fatty liver disease detection

Fatty liver disease is a common disease that causes extra fat storage in an individual's liver. Patients with fatty liver disease may progress to cirrhosis and liver failure, further leading to liver cancer. The prevalence of fatty liver disease ranges from 10% to 30% in many countries. In general, detecting fatty liver requires professional neuroimaging modalities or methods such as computed tomography, ultrasound, and medical experts' practical experiences. Considering this point, finding intelligent electronic noninvasive diagnostic approaches are desired at present. Currently, most existing works in the area of computerized noninvasive disease detection often apply one view (modality) or perform multi-view (several modalities) analysis, e.g., face, tongue, and/or sublingual for disease detection. The multi-view data of patients provides more complementary information for diagnosis. However, due to the conditions of data acquisition, interference by human factors, etc., many multi-view data are defective with some missing-view information, making these multi-view data difficult to evaluate. This factor largely affects the performance of classifying disease and the development of fully computerized noninvasive methods. Thus, the purpose of this study is to address the missing view issue among noninvasive disease detection. In this work, a multi-view dataset containing facial, sublingual vein, and tongue images are initially processed to produce corresponding feature for incomplete multi-view disease diagnostic evaluation. Hereby, we propose a novel method, i.e., multi-view completion, to process the incomplete multi-view data in order to complete the missing-view information for classifying fatty liver disease from healthy candidates. In particular, this method can explore the intra-view and inter-view information to produce the missing-view data effectively. Extensive experiments on a collected dataset with 220 fatty liver patients and 220 healthy samples show that our proposed approach achieves better diagnostic results with missing-view completion compared to the original incomplete multi-view data under various classifiers. Related results prove that our method can effectively process the missing-view issue and improve the noninvasive disease detection performance.

Deep Learning Based Tongue Prickles Detection in Traditional Chinese Medicine

Tongue diagnosis is a convenient and noninvasive clinical practice of traditional Chinese medicine (TCM), having existed for thousands of years. Prickle, as an essential indicator in TCM, appears as a large number of red thorns protruding from the tongue. The term “prickly tongue” has been used to describe the flow of qi and blood in TCM and assess the conditions of disease as well as the health status of subhealthy people. Different location and density of prickles indicate different symptoms. As proved by modern medical research, the prickles originate in the fungiform papillae, which are enlarged and protrude to form spikes like awn. Prickle recognition, however, is subjective, burdensome, and susceptible to external factors. To solve this issue, an end-to-end prickle detection workflow based on deep learning is proposed. First, raw tongue images are fed into the Swin Transformer to remove interference information. Then, segmented tongues are partitioned into four areas: root, center, tip, and margin. We manually labeled the prickles on 224 tongue images with the assistance of an OpenCV spot detector. After training on the labeled dataset, the super-resolutionfaster-RCNN extracts advanced tongue features and predicts the bounding box of each single prickle. We show the synergy of deep learning and TCM by achieving a 92.42% recall, which is 2.52% higher than the previous work. This work provides a quantitative perspective for symptoms and disease diagnosis according to tongue characteristics. Furthermore, it is convenient to transfer this portable model to detect petechiae or tooth-marks on tongue images.

Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition

The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis. The clinical manifestations of patients with tooth-marked tongue include loss of appetite, borborygmus, gastric distention, and loose stool. Traditional clinical tooth-marked tongue recognition is conducted subjectively based on the doctor’s visual observation, and its performance is affected by the doctor’s subjectivity, experience, and environmental lighting changes. In addition, the tooth marks typically have various shapes and colors on the tongue, which make it very challenging for doctors to identify tooth marks. The existing methods based on deep learning have made great progress for tooth-marked tongue recognition, but there are still shortcomings such as requiring a large amount of manual labeling of tooth marks, inability to detect and locate the tooth marks, and not conducive to clinical diagnosis and interpretation. In this study, we propose an end-to-end deep neural network for tooth-marked tongue recognition based on weakly supervised learning. Note that the deep neural network only requires image-level annotations of tooth-marked or non-tooth marked tongues. In this method, a deep neural network is trained to classify tooth-marked tongues with the image-level annotations. Then, a weakly supervised tooth-mark detection network (WSTDN) as an architecture variant of the pre-trained deep neural network is proposed for the tooth-marked region detection. Finally, the WSTDN is re-trained and fine-tuned using only the image-level annotations to simultaneously realize the classification of the tooth-marked tongue and the positioning of the tooth-marked region. Experimental results of clinical tongue images demonstrate the superiority of the proposed method compared with previously reported deep learning methods for tooth-marked tongue recognition. The proposed tooth-marked tongue recognition model may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms.

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.

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.

Application of computer tongue image analysis technology in the diagnosis of NAFLD

Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882-0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.

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.

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

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

Tongue diagnosis indices for gastroesophageal reflux disease: A cross-sectional, case-controlled observational study

Traditional Chinese medicine tongue diagnosis can mirror the status of the internal organ, but evidence is lacking regarding the accuracy of tongue diagnosis to gastroesophageal reflux disease (GERD). This study was to investigate the association between GERD and tongue manifestation, and whether tongue imaging could be initial diagnosis of GERD noninvasively.We conducted a cross-sectional, case-controlled observational study at Kaohsiung Chang Gung Memorial Hospital in Taiwan from January 2016 to September 2017. Participants aged over 20 years old with GERD were enrolled and control group without GERD were matched by sex. Tongue imaging were acquired with automatic tongue diagnosis system, then followed by endoscope examination. Nine tongue features were extracted, and a receiver operating characteristic (ROC) curve, analysis of variance, and logistic regression were used.Each group enrolled 67 participants. We found that the saliva amount (P = .009) and thickness of the tongue's fur (P = .036), especially that in the spleen-stomach area (%) (P = .029), were significantly greater in patients with GERD than in those without. The areas under the ROC curve of the amount of saliva and tongue fur in the spleen-stomach area (%) were 0.606 ± 0.049 and 0.615 ± 0.050, respectively. Additionally, as the value of the amount of saliva and tongue fur in the spleen-stomach area (%) increased, the risk of GERD rose by 3.621 and 1.019 times, respectively. The tongue fur in the spleen-stomach area (%) related to severity of GERD from grade 0 to greater than grade B were 51.67 ± 18.72, 58.10 ± 24.60, and 67.29 ± 24.84, respectively.The amount of saliva and tongue fur in the spleen-stomach area (%) might predict the risk and severity of GERD and might be noninvasive indicators of GERD. Further large-scale, multi-center, randomized investigations are needed to confirm the results.Trial registration: NCT03258216, registered August 23, 2017.

Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine

Background

In this study, we focus on building a fine-grained entity annotation corpus with the corresponding annotation guideline of traditional Chinese medicine (TCM) clinical records. Our aim is to provide a basis for the fine-grained corpus construction of TCM clinical records in future.

Methods

We developed a four-step approach that is suitable for the construction of TCM medical records in our corpus. First, we determined the entity types included in this study through sample annotation. Then, we drafted a fine-grained annotation guideline by summarizing the characteristics of the dataset and referring to some existing guidelines. We iteratively updated the guidelines until the inter-annotator agreement (IAA) exceeded a Cohen’s kappa value of 0.9. Comprehensive annotations were performed while keeping the IAA value above 0.9.

Results

We annotated the 10,197 clinical records in five rounds. Four entity categories involving 13 entity types were employed. The final fine-grained annotated entity corpus consists of 1104 entities and 67,799 tokens. The final IAAs are 0.936 on average (for three annotators), indicating that the fine-grained entity recognition corpus is of high quality.

Conclusions

These results will provide a foundation for future research on corpus construction and named entity recognition tasks in the TCM clinical domain.

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.

The tongue features associated with type 2 diabetes mellitus

Diabetes mellitus (DM) is a public problem closely associated with numerous oral complications, such as coated tongue, xerostomia, salivary dysfunction, etc. Tongue diagnosis plays an important role in clinical prognosis and treatment of diabetes in the traditional Chinese medicine (TCM). This study investigated discriminating tongue features to distinguish between type 2 DM and non-DM individuals through non-invasive TCM tongue diagnosis.The tongue features for 199 patients with type 2 DM, and 372 non-DM individuals, serving as control, are extracted by the automatic tongue diagnosis system (ATDS). A total of 9 tongue features, namely, tongue shape, tongue color, fur thickness, fur color, saliva, tongue fissure, ecchymosis, teeth mark, and red dot. The demography, laboratory, physical examination, and tongue manifestation data between 2 groups were compared.Patients with type 2 DM possessed significantly larger covering area of yellow fur (58.5% vs 22.5%, P < .001), thick fur (50.8% vs 29.2%, P < .001), and bluish tongue (P < .001) than those of the control group. Also, a significantly higher portion (72.7% vs 55.2%, P < .05) of patients with long-term diabetics having yellow fur color than the short-term counterparts was observed.The high prevalence of thick fur, yellow fur color, and bluish tongue in patient with type 2 DM revealed that TCM tongue diagnosis can serve as a preliminary screening procedure in the early detection of type 2 DM in light of its simple and non-invasive nature, followed by other more accurate testing process. To the best of our knowledge, this is the first attempt in applying non-invasive TCM tongue diagnosis to the discrimination of type 2 DM patients and non-DM individuals.

Tongue diagnosis indices for upper gastrointestinal disorders: Protocol for a cross-sectional, case-controlled observational study

BACKGROUND

Upper gastrointestinal disorders are common in clinical practice, for example, gastritis, peptic ulcer disease, and gastroesophageal reflux disease. Panendoscopy or upper gastrointestinal endoscopy is viewed as the primary tool for examining the upper gastrointestinal mucosa, and permitting biopsy and endoscopic therapy. Although panendoscopy is considered to be a safe procedure with minimal complications, there are still some adverse effects, and patients are often anxious about undergoing invasive procedures. Traditional Chinese medicine 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.

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 upper gastrointestinal disorders. Volunteers over 20 years old with and without upper gastrointestinal symptoms will be enrolled. Tongue images will be captured and the patients divided into 4 groups according to their panendoscopy reports, including a gastritis group, peptic ulcer disease group, gastroesophageal reflux disease 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.

OBJECTIVES

The aim of this protocol is to apply a noninvasive ATDS to evaluate tongue manifestations of patients with upper gastrointestinal disorders and examine its efficacy as a diagnostic tool.

The association between objective tongue color and endoscopic findings: results from the Kyushu and Okinawa population study (KOPS)

Background

The relation between tongue color and gastroesophageal disease is unclear. This study was done to investigate the associations between tongue color (TC), endoscopic findings, Helicobacter.pylori infection status, and serological atrophic gastritis (SAG).

Methods

The participants were 896 residents of Ishigaki Island, Okinawa, aged 28–86 years. The tongue was photographed, esophagogastroduodenoscopy was done, and serum antibody to H.pylori was measured. SAG was defined as a serum Pepsinogen (PG)Ilevel ≤70 ng/ml and a PGI/IIratio ≤3.0. TC was measured by the device-independent international commission on Illumination 1976 L*a*b* color space standards at four points: (1) edge, (2) posterior, (3) middle, and (4) apex. We also calculated the ratio of the tongue edge to the three other measured points to examine the association between the coating of the tongue and the endoscopic and laboratory findings.

Results

Participants were excluded who had two or more endoscopic findings (n = 315) or who had SAG without seropositivity to H.pylori (n = 33). The remaining 548 participants were divided into three groups: SAG and seropositive to H.pylori (n = 67), seropositive to H.pylori alone (n = 56), and without SAG and seronegative for H.pylori (n = 425). We divided 425 residents into a single endoscopic finding positive group (n = 207) and a negative group, which served as a control (n = 218). The most frequent single endoscopic finding was esophageal hernia (n = 110), followed by erosive esophagitis (n = 35) and erosive gastritis (EG) (n = 45). EH was significantly associated with TC (2b*/1b*) (P < 0.05). EG was significantly associated with TC (3a*, 3b*) (P < 0.05). Seropositivity to H.pylori was significantly associated with TC (3 L*, 3 L*/1 L*) (P < 0.05, <0.01), and seropositivity to both H.pylori and SAG was significantly associated with TC (3 L*/1 L*) (P < 0.05). Multivariate analysis extracted TC (3a*, 3b*) as an independent factor associated with a differential diagnosis of EG (Odds ratio (OR) 2.66 P = 0.008, OR 2.17 P = 0.045).

Conclusions

The tongue body color of the middle area reflects acute change of gastric mucosa, such as erosive gastritis. Tongue diagnosis would be a useful, non-invasive screening tool for EG.

TCM tongue diagnosis index of early-stage breast cancer

OBJECTIVES

This paper investigates discriminating tongue features to distinguish between early stage breast cancer (BC) patients and non-breast cancer individuals through non-invasive traditional Chinese medicine (TCM) tongue diagnosis.

DESIGN

The tongue features for 67 patients with 0 and 1 stages of BC, and 70 non-breast cancer individuals are extracted by the automatic tongue diagnosis system (ATDS). A total of nine tongue features, namely, tongue color, tongue quality, tongue fissure, tongue fur, red dot, ecchymosis, tooth mark, saliva, and tongue shape are identified for each tongue. Features extracted are further sub-divided according to the areas located, i.e., spleen-stomach, liver-gall-left, liver-gall-right, kidney, and heart-lung areas. This study focuses on deriving significant tongue features (p<0.05) to discriminate early-stage BC patients from non-breast cancer individuals.

RESULTS

The Mann-Whitney test shows that the amount of tongue fur (p=0.024), maximum covering area of tongue fur (p=0.009), thin tongue fur (p=0.009), the average area of red dot (p=0.049), the maximum area of red dot (p=0.009), red dot in the spleen-stomach area (p=0.000), and red dot in the heart-lung area (p=0.000) demonstrate significant differences. The data collected are further classified into two groups. The training group consists of 57 early-stage BC patients and 60 non-breast cancer individuals, while the testing group is composed of 10 early-stage BC patients and 10 non-breast cancer individuals. The logistic regression by utilizing these 7 tongue features with significant differences in Mann-Whitney test as factors is performed. In order to reduce the number of tongue features employed in prediction, tongue features with the least amount of significant difference, namely, maximum area of red dot and average area of red dot, are removed progressively. The tongue features of the testing group are employed in the aforementioned three models to test the power of significant tongue features identified in predicting early-stage BC. An accuracy of 80%, 80% and 90% is reached on non-breast cancer individuals by applying the 7, 6 and 5 significant tongue features obtained through Mann-Whitney test, respectively, while 60%, 60% and 50% is reached on the corresponding early-stage BC patients.

CONCLUSION

The TCM tongue diagnosis can serve as a preliminary screening procedure in the early detection of BC in light of its simple and non-invasive nature, followed by other more accurate testing process. To the best of our knowledge, this is the first attempt in applying non-invasive TCM tongue diagnosis to the discrimination of early-stage BC patients and non-breast cancer individuals.