• The korean journal of orthodontics
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• v.2 no.1
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• pp.15-21
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• 1971

부정교합과 구강영역의 악습관과의 관계를 구명해 보고자 본 저자는 구내 악습중 비교적 발생빈도가 높으며 부정교합에 미치는 영향이 크다고 생각되는 tongue-thrusting에 대한 다음과 같은 일연의 조사를 시행하였다. 1. Tongue-thrusting의 빈도와 부정교합의 유형과의 관계를 조사하였다. 2. 서울대학교 치과대학 부속병원에 내원한 263명의 부정교합 환자로부터 tongue-thrusting의 유무, 수유방법, 구내악습 및 상기도병변상태를 관찰하였다. 결과는 다음과 같았다. 1. 15세 내실 17세 남학생 1,356명중 tongue-thrusting을 보인 사람은 $12.7\%$였다. 2. Angle씨 3급 불정교합이 tongue-thrust swallowing과 가장 밀접한 관계가 있었다. 3. 인공 수유가 tongue-thrust swallowing의 원인이 된다는 명확한 근거는 없었다. 4. 상기도의 만성 병변은 tongue-thrust swallowing과 무관하였다.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.99-106
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• 2019

In traditional Chinese medical (TCM) science, tongue images can be observed for medical diagnosis; however, the tongue diagnosis of TCM is influenced by the subjective factors of doctors, and the diagnosis results vary from person to person. Quantitative TCM tongue diagnosis can improve the accuracy of diagnosis and increase the application value. In this paper, digital image processing and pattern recognition technologies are employed on mobile device to classify tongue images collected in different health states. First, through grayscale integral projection processing, the trough is found to localize the tongue body. Then the tongue body image is transferred from RGB color space to HSV color space, and the average H and S values are considered as the color features. Finally, the diagnosis results are obtained according to the relationship between the color characteristics and physical symptoms.

• Journal of Oral Medicine and Pain
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• v.45 no.4
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• pp.120-123
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• 2020

The causes of tongue pain and discomfort include systemic disease, malnutrition, mental illness, fungal infection, and neuropathy. Three postmenopausal women reported burning sensations and stiffness of the tongue for various periods, from one month to four years. There were no objective etiological factors to cause the tongue pain and discomfort. Muscular tenderness upon palpation of masticatory muscles, sternocleidomastoid, trapezius, and tongue were observed. Physical therapy approaches such as moist hot pack, ultrasound, and myomonitor were performed on three patients with tongue pain, just as for temporomandibular joint disease. Additional botulinum toxin injection therapy was applied to one patient who displayed a clenching habit. All three patients showed a marked improvement in their tongue symptoms after the muscle relaxation and botulinum toxin injection therapy.

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.45-55
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• 2013

In this study, we propose a tongue diagnosis system which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue fur ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas and the distribution of tongue coating of six areas is examined by SVM. For SVM, we use a 3-dimensional vector calculated by PCA from a 12-dimensional vector consisting of RGB, HSV, Lab, and Luv. As a result, we stably detected the tongue area using ASM. Furthermore, we recognized that PCA and SVM helped to raise the ratio of tongue coating detection.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.5
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• pp.320-326
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• 2016

Tongue diagnosis is convenient and non-invasive method to examine the body's functional condition, and it has been frequently used in traditional Korean Medicine (KM). The aim of this study was to investigate the difference of the tongue color assessed by computerized tongue image analysis system (CTIS) between the common cold (CC) patients and healthy subjects. A total of 85 participants, including 45 CC patients without organic diseases and 40 healthy subjects, were asked to complete the CC symptom questionnaire. A tongue image was acquired by using CTIS. Color differences in Commission Internationale de l'Eclairage (CIE) L*, a* and b* between the CC patient group and the control group were analyzes by using paired t-test analysis. The variable CIE b* of the tongue body was significantly lower in CC than that in controls (P=0.019). The variable CIE L* of the tongue coating was significantly higher in CC than that in controls (P=0.032). In CC, the color of the tongue body seems to be changed to intense red color. The color of the tongue coating seems to be changed to thick fur. The present study demonstrated that the CTIS can be used as a diagnostic and monitoring tool for the objective and standardized evaluation of common cold in clinics.

• The Journal of Korean Medicine
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• v.33 no.4
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• pp.1-8
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• 2012

Objectives: The purpose of this study was to specify major tongue diagnostic indicators and evaluate their significance in discriminating pattern identification subtypes in stroke patients. Methods: This study used a community based multi-center observational design. Participants (n=1,502) were stroke patients admitted to 11 oriental medical university hospitals between December 2006 and February 2010. To determine which tongue indicator affected each pattern identification, a decision tree analysis of the chi-square automatic interaction detector (CHAID) algorithm was performed. The chi-squared test was used as the criterion in splitting data with a p-value less than 0.05 for division, which is the main procedure for developing a decision tree. The minimum sample size for each node was specified as n =10, and branching was limited to two levels. Results: From the 9 tongue diagnostic indicators, 6 major tongue indicators (red tongue, pale tongue, yellow fur, white fur, thick fur, and teeth-marked tongue) were identified through the decision tree analysis. Furthermore, each pattern identification was composed of specific combinations of the 6 major tongue indicators. Conclusions: This study suggests that the 6 tongue indicators identified through the decision tree analysis can be used to discriminate pattern identification subtypes in stroke patients. However, it is still necessary to re-evaluate other pattern identification indicators to further the objectivity and reliability of traditional Korean medicine.

• Journal of dental hygiene science
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• v.20 no.4
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• pp.206-212
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• 2020

Background: A Teachable Machine is a kind of machine learning web-based tool for general persons. In this paper, the feasibility of Google's Teachable Machine (ver. 2.0) was studied in the diagnosis of the tooth-marked tongue. Methods: For machine learning of tooth-marked tongue diagnosis, a total of 1,250 tongue images were used on Kaggle's web site. Ninety percent of the images were used for the training data set, and the remaining 10% were used for the test data set. Using Google's Teachable Machine (ver. 2.0), machine learning was performed using separated images. To optimize the machine learning parameters, I measured the diagnosis accuracies according to the value of epoch, batch size, and learning rate. After hyper-parameter tuning, the ROC (receiver operating characteristic) analysis method determined the sensitivity (true positive rate, TPR) and specificity (false positive rate, FPR) of the machine learning model to diagnose the tooth-marked tongue. Results: To evaluate the usefulness of the Teachable Machine in clinical application, I used 634 tooth-marked tongue images and 491 no-marked tongue images for machine learning. When the epoch, batch size, and learning rate as hyper-parameters were 75, 0.0001, and 128, respectively, the accuracy of the tooth-marked tongue's diagnosis was best. The accuracies for the tooth-marked tongue and the no-marked tongue were 92.1% and 72.6%, respectively. And, the sensitivity (TPR) and specificity (FPR) were 0.92 and 0.28, respectively. Conclusion: These results are more accurate than Li's experimental results calculated with convolution neural network. Google's Teachable Machines show good performance by hyper-parameters tuning in the diagnosis of the tooth-marked tongue. We confirmed that the tool is useful for several clinical applications.

• Journal of Korean Traditional Oncology
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• v.27 no.1
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• pp.13-23
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• 2022

Objective: The purpose of this review is to analyze the clinical studies on tongue diagnosis in cancer patients. Methods: Domestic and foreign databases were used, such as Pubmed, google scholar, Wanfang med online, Scopus, and OASIS. Searching keywords were tongue diagnosis, tongue color, tongue fur, tongue inspection, cancer, tumor, neoplasm, carcinoma, etc. Studies on tongue diagnosis in cancer patients were included. The published year was limited from 2000 to June 2022. Results: Thirteen studies were enrolled. All selected studies were cross-sectional studies. Cancer patients tend to have a dark and blue-purple tongue, thick fur, yellow fur, fissure tongue, and red dots on the tongue compared with non-cancer patients. With the aggravation of cancer, the rate of patients having dark or blue, or purple tongues increased, and the patients' sublingual veins became wide and tortuous. Conclusion: This study suggests that cancer patients tend to have distinct features of tongue diagnosis. Further researches are warranted.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.123-131
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• 2008

In Oriental medicine, the status of a tongue is the important indicator to diagnose the condition of one's health like the physiological and the clinicopathological changes of internal organs in a body. A tongue diagnosis is not only convenient but also non-invasive, and therefore widely used in Oriental medicine. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition a lot. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue region from a facial image captured and classifying tongue coating are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth are similar. The proposed method includes preprocessing, over-segmenting, detecting the edge with a local minimum over a shading area from the structure of a tongue, correcting local minima or detecting the edge with the greatest color difference, selecting one edge to correspond to a tongue shape, and smoothing edges, where preprocessing consists of down-sampling to reduce computation time, histogram equalization, and edge enhancement, which produces the region of a segmented tongue. Finally, the systematic procedure separated only a tongue region from a face image with a tongue, which was obtained from a digital tongue diagnosis system. Oriental medical doctors' evaluation for the results illustrated that the segmented region excluding a non-tongue region provides important information for the accurate diagnosis. The proposed method can be used for an objective and standardized diagnosis and for an u-Healthcare system.

• Korean Journal of Veterinary Research
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• v.36 no.2
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• pp.255-264
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• 1996

This studies were carried out to identify the characteristics of the tongue of Korean native goat(Capra hircus) by macroscopy, microscopy and scanning microscopy. Korean native goat had torus linguae, median lingual sulcus, lingual fossa and ventral median fissure but did not have glossoepiglottic fold and terminal sulcus in the tongue. The whole length of tongue was $11.51{\pm}0.76cm$. The length of tongue apex, tongue body, tongue root and the torus linguae were $2.62{\pm}0.28$, $7.39{\pm}0.27$, $1.56{\pm}0.26$ and $6.37{\pm}0.29cm$, respectively. The width of tongue apex, torus linguae and tongue root were $3.41{\pm}0.24$, $3.74{\pm}0.29$ and $3.68{\pm}0.11$, respectively. The thickness of tongue apex was $1.60{\pm}0.10$, and the height of torus linguae was $1.52{\pm}0.15cm$. Filiform papillae were present at the tongue apex and the tongue body rostral to torus linguae. Fungiform papillae were scattered from tongue apex to rostral portion of torus linguae, being in abundance at the tongue apex. Vallate papillae were showed at the lateral portion of torus linguae, while lentiform papillae were present at its central portion. Conical papillae were located between vallate and lentiform papillae. The numbers of filiform, fungiform, conical, vallate and lentiform papillae were $46,980{\pm}1070.98$, $446.8{\pm}36.97$, $818.4{\pm}43.99$, $34.8{\pm}2.77$, and $255.6{\pm}39.30$, respectively. The average numbers of taste bud were $8.3{\pm}2.04$ in a fungiform papilla and $247.3{\pm}37.44$ in a vallate papilla. The filiform papilla had secondary and tertiary papillae. The height of filiform papilla was about $150{\mu}m$ and the diameter was $100{\mu}m$. The diameters of fungiform papillae were 350 to $550{\mu}m$. The long and short diameters of maximum-sized lentiform papilla were 4000 and $3000{\mu}m$, respectively, while those of minimum-sized papilla were 700 and $600{\mu}m$, respectively. The height of conical papillae was 450 to $600{\mu}m$ and diameter was 250 to $450{\mu}m$. The vallate papilla was round or oval in shape and its diameter was 500 to $850{\mu}m$. It had well-developed papillary groove around itself. The modified conical papillae were not observed in the tongue of Korean native goat.