• 대한치과교정학회지
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• 제2권1호
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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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• 제6권2호
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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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• 제45권4호
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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.

• 한국컴퓨터정보학회논문지
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• 제18권4호
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• pp.45-55
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• 2013

본 논문에서는 설진을 위하여 얼굴 영상으로부터 혀 영역을 추출하고, 혀 영역을 6개 세부 영역으로 분할한 다음 영역별 설태 비율을 검출하는 방법을 제안한다. 얼굴 영상으로부터 혀 영역을 추출하기 위해 능동적 형태 모델방법의 하나인 ASM을 이용하였다. 검출된 혀 영역을 한의학에서 사용하는 일반적인 6개 영역으로 분할하였고, 분할된 영역 내에서의 설태 분포 정도를 SVM을 이용하여 검출하였다. SVM 분류 시 특징 벡터로는 RGB, HSV, Lab, Luv로 구성된 12차원의 벡터로부터 주성분 분석을 통하여 구해진 3차원의 벡터를 사용하였다. 실험 결과 ASM을 사용하여 혀 영역을 안정적으로 검출할 수 있었고 주성분 분석과 SVM을 활용함으로써 설태 검출율이 높아짐을 알 수 있었다.

• 동의생리병리학회지
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• 제30권5호
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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.

• 대한한의학회지
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• 제33권4호
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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.

• 치위생과학회지
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• 제20권4호
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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.

• 대한암한의학회지
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• 제27권1호
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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.

• 전자공학회논문지SC
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• 제45권6호
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• pp.123-131
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• 2008

한의학에서 혀의 상태는 인체 내부의 생리적 병리적 변화와 같은 건강 상태를 진단하는 중요한 지표로 활용된다. 혀의 상태를 진단하는 방법(설진)은 편리할 뿐 아니라 비침습적이므로, 한의학에서 널리 활용되고 있다. 하지만, 설진은 광원이나 환자의 자세, 의사의 건강 조건과 같은 검사 환경에 따라 많은 영향을 받는다. 객관적이고 표준화된 진단을 위한 자동 설진 시스템을 개발하기 위하여 촬영된 얼굴 영상으로부터 혀를 영역분할하고 설태를 분류하는 것은 필수적이지만 혀와 입술, 입 근처의 피부색이 서로 유사하므로 쉽지 않은 일이다. 제안된 방법은 전처리 과정과 영역분할, 혀의 구조로부터 발생하는 음영 영역의 지역 최소값 위치 검색, 지역 최소값의 교정, 컬러의 차이를 최대로 하는 위치를 찾는 컬러 경계면 탐색, 척의 기하적인 특성에 일치하는 경계면 선택, 경계면 평활화로 구성되어 있으며, 여기서 전처리 과정은 계산량의 감소를 위한 부 표본화, 히스토그램 평활화, 경계면 강화를 수행한다. 이러한 시스템적인 과정을 거치면, 영역분할된 혀를 획득할 수 있게 된다. 제안된 방법으로 분할된 영역은 초과적으로 혀가 아닌 영역을 제외해 낼 뿐 아니라 정확한 진단을 위해 중요한 정보를 제공함을 한의사의 진단 유효도 평가점수를 통해 확인할 수 있었다. 제안된 방법은 진단의 객관화와 표준화에 기여할 뿐만 아니라 u-Healthcare 시스템에도 활용 가능하다.

• 대한수의학회지
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• 제36권2호
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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.