Automated tongue diagnosis on the smartphone and its applications

Hu, Min; Lan, Kun; Fang, Wen Chieh; Huang, Yu Chia; Ho, Tsung Jung; Lin, Chun-Ping; Yeh, Ming Hsien; Raknim, Paweeya; Lin, Ying; Cheng, Ming; He, Yi; Tseng, Kuo‐Chih

doi:10.1016/j.cmpb.2017.12.029

Cited by 40 publications

(24 citation statements)

References 15 publications

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“…The first strength is in its novelty with the use of smartphones without any color correction measures. It is generally believed that the use of smartphones instead of a purpose-built tongue imaging system would produce unreliable results, due to variations in factors such as camera settings, light source, and display settings [ 16 ]. However, we demonstrated, for the first time, that using our tongue photographic protocol, it is possible to obtain good quality tongue images with a good to very good level of intra-rater agreement against direct tongue inspection, and with a moderate level of inter-rater agreement on fur features.…”

Section: Discussionmentioning

confidence: 99%

“…In general, TCM diagnosis, including tongue diagnosis, has been found to be subjective with low inter-rater reliability [ 12 - 15 ]. Diagnosis made from tongue images collected by mobile devices may be more variable, due to variations in photographic techniques, camera settings, environmental lighting, light sources, display devices, etc [ 16 ]. These variations present barriers in the direct use of smartphone-collected tongue images in telemedicine and the application of machine learning for automatic tongue diagnosis in mHealth.…”

Section: Introductionmentioning

confidence: 99%

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Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study

Wang¹,

Zhang²,

Yuen³

et al. 2020

JMIR Mhealth Uhealth

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Background There is a growing trend in the use of mobile health (mHealth) technologies in traditional Chinese medicine (TCM) and telemedicine, especially during the coronavirus disease (COVID-19) outbreak. Tongue diagnosis is an important component of TCM, but also plays a role in Western medicine, for example in dermatology. However, the procedure of obtaining tongue images has not been standardized and the reliability of tongue diagnosis by smartphone tongue images has yet to be evaluated. Objective The first objective of this study was to develop an operating classification scheme for tongue coating diagnosis. The second and main objective of this study was to determine the intra-rater and inter-rater reliability of tongue coating diagnosis using the operating classification scheme. Methods An operating classification scheme for tongue coating was developed using a stepwise approach and a quasi-Delphi method. First, tongue images (n=2023) were analyzed by 2 groups of assessors to develop the operating classification scheme for tongue coating diagnosis. Based on clinicians’ (n=17) own interpretations as well as their use of the operating classification scheme, the results of tongue diagnosis on a representative tongue image set (n=24) were compared. After gathering consensus for the operating classification scheme, the clinicians were instructed to use the scheme to assess tongue features of their patients under direct visual inspection. At the same time, the clinicians took tongue images of the patients with smartphones and assessed tongue features observed in the smartphone image using the same classification scheme. The intra-rater agreements of these two assessments were calculated to determine which features of tongue coating were better retained by the image. Using the finalized operating classification scheme, clinicians in the study group assessed representative tongue images (n=24) that they had taken, and the intra-rater and inter-rater reliability of their assessments was evaluated. Results Intra-rater agreement between direct subject inspection and tongue image inspection was good to very good (Cohen κ range 0.69-1.0). Additionally, when comparing the assessment of tongue images on different days, intra-rater reliability was good to very good (κ range 0.7-1.0), except for the color of the tongue body (κ=0.22) and slippery tongue fur (κ=0.1). Inter-rater reliability was moderate for tongue coating (Gwet AC2 range 0.49-0.55), and fair for color and other features of the tongue body (Gwet AC2=0.34). Conclusions Taken together, our study has shown that tongue images collected via smartphone contain some reliable features, including tongue coating, that can be used in mHealth analysis. Our findings thus support the use of smartphones in telemedicine for detecting changes in tongue coating.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study

Wang¹,

Zhang²,

Yuen³

et al. 2020

JMIR Mhealth Uhealth

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show abstract

“…Third, further researches are required to investigate the tongue image quality assessment model for more diverse scenes, such as smartphones [34,35], aiming at various kinds of bad quality tongue image appearances. Constructing multiple classi cation models to distinguish bad tongue quality images into more groups may increase the clinical applicability to expand the clinical application level of tongue diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Tongue Image Quality Assessment Based on Deep Convolutional Neural Network

Jiang

Yao

et al. 2020

Preprint

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Background: With the wide application of digital tongue diagnosis instrument, massive tongue images will be produced. Adequate image quality is the prerequisite to ensure accurate tongue image analysis. In the process of tongue image collection, improper operation may lead to many poor-quality images (fogging, underexposure, overexposure, blurred focus, wrong tongue posture, etc.), which seriously affect the image processing and the accuracy of image analysis. However traditional pattern recognition is difficult to evaluate the quality of tongue images by extracting features and manual removal of tongue images with bad quality consumes a lot of labor and has a high error rate. In this research, we utilized a deep convolutional neural network to automatically select bad quality tongue images.Methods: The present study was conducted to identify the most appropriate CNN model for Tongue Image Quality Assessment based on deep CNN. The CNN model was evaluated by using Residual neural network and compared with VGGNet and DenseNet. Evaluation metrics such as accuracy, precision, recall, and F1-score were used for CNN model performance.Results: A detection model is established for tongue image quality control based on deep residual network, with an average accuracy of 99.04%, accuracy of 99.05%, recall of 99.04%, and F1-score of 99.05%, which can be used for quality screening of massive tongue images.Conclusions: Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and prove that applying deep learning methods, specifically deep CNN, to evaluate bad quality tongue images is feasible.

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“…According to East Asian Medicine, the internal organs such as liver, stomach, lung, heart, and urinary bladder meridians were directly connected to the corresponding reflex zones of the tongue [32]. Hu et al have observed that tongue features are strongly correlated with the alanine aminotransferase (ALT) and aspartate aminotransferase (AST) which are important biomarkers for liver diseases [33]. Nakamura et al have found a mass of neurofibroma in the tongue which indicates von Recklinghausen disease, which is an autosomal dominant neurogenetic disorder [34].…”

Section: Introductionmentioning

confidence: 99%

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Thirunavukkarasu

Snekhalatha

Krishnan

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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Diabetes mellitus is one of the life threatening diseases over the globe, and an early prediction of diabetes is of utmost importance in this current scenario. International Diabetes Federation (IDF) reported nearly half of the world’s population was undiagnosed and unaware of being developed into diabetes. In 2017, around 84 million individuals were living with diabetes, and it might increase to 156 million by the end of 2045 stated by IDF. Generally, the diagnosis of diabetes relies on the biochemical method that may cause uneasiness and probability of infections to the subjects. To overcome such difficulties, a noninvasive method is much needed around the globe for primary screening. A change in body temperature is an indication of various diseases. Infrared thermal imaging is relatively a novel technique for skin temperature measurement and turned out to be well known in the medical field due to being noninvasive, risk-free, and repeatable. According to traditional Chinese medicine, the human tongue is a sensitive mirror that reflects the body’s pathophysiological condition. So, we have (i) analysed and classified diabetes based on thermal variations at human tongue, (ii) segmented the hot spot regions from tongue thermogram by RGB (red, green, blue) based color histogram image segmentation method and extracted the features using gray level co-occurrence matrix algorithm, (iii) classified normal and diabetes using various machine learning algorithms, and (iv) developed computer aided diagnostic system to classify diabetes mellitus. The baseline measurements and tongue thermograms were obtained from 140 subjects. The measured tongue surface temperature of the diabetic group was found to be greater than normal. The statistical correlation between the HbA1c and the thermal distribution in the tongue region was found to be r2 = 0.5688. The Convolutional Neural Network has outperformed the other classifiers with 94.28% accuracy rate. Thus, tongue thermograms could be used as a preliminary screening approach for diabetes prognosis.

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Automated tongue diagnosis on the smartphone and its applications

Cited by 40 publications

References 15 publications

Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study

Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study

Tongue Image Quality Assessment Based on Deep Convolutional Neural Network

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

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