Computational tongue color simulation in tongue diagnosis

Chen, Hung Shing; Chen, Shih Ming; Jiang, Chen Yu; Zhang, Yi Chen; Lin, Chia Yu; Lin, Chia En; Lee, Jen Ai

doi:10.1002/col.22717

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…CIE L * a * b * value was applied to quantify the tongue color and classify its color type. 120 Another statistical feature of wide line on the tongue image would do a great favor in classifying cracked tongue. 117 Features of too many irrelevant areas may negatively impact discrimination tasks.…”

Section: Tongue Image For the Symptom (Sign) Differentiationmentioning

confidence: 99%

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Liu

Peng

et al. 2023

DIGITAL HEALTH

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The rapid development of artificial intelligence technology has gradually extended from the general field to all walks of life, and intelligent tongue diagnosis is the product of a miraculous connection between this new discipline and traditional disciplines. We reviewed the deep learning methods and machine learning applied in tongue image analysis that have been studied in the last 5 years, focusing on tongue image calibration, detection, segmentation, and classification of diseases, syndromes, and symptoms/signs. Introducing technical evolutions or emerging technologies were applied in tongue image analysis; as we have noticed, attention mechanism, multiscale features, and prior knowledge were successfully applied in it, and we emphasized the value of combining deep learning with traditional methods. We also pointed out two major problems concerned with data set construction and the low reliability of performance evaluation that exist in this field based on the basic essence of tongue diagnosis in traditional Chinese medicine. Finally, a perspective on the future of intelligent tongue diagnosis was presented; we believe that the self-supervised method, multimodal information fusion, and the study of tongue pathology will have great research significance.

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Section: Tongue Image For the Symptom (Sign) Differentiationmentioning

confidence: 99%

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Liu

Peng

et al. 2023

DIGITAL HEALTH

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“…36) Therefore, tongue evaluation represents an important aspect of telemedicine; thus, a method for computerizing and automating tongue diagnosis/evalution is being studied. 37,38) The viscosity, quantity, and color of sputum are other important qualities to use for obtaining a diagnosis 39) ; Chinese and Korean COVID-19 medical guidelines also suggest using the appearance of sputum and the condition of the tongue as distinguishing points for symptoms. 5,8) Although an evaluation of the tongue and sputum could not be performed owing…”

Section: Changes In Covid-19-relatedmentioning

confidence: 99%

Management of asymptomatic to mild COVID-19 patients with Cheongpebaedok-tang on the telemedical basis: A retrospective observational case series

Kang,

Kim,

Song

et al. 2023

J Korean Med

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Objectives: This retrospective observational study aimed to investigate the efficacy and safety of Cheongpebaedok-tang, a traditional Korean herbal medicine, provided via telemedicine to patients with asymptomatic to mild COVID-19 in Korea.Methods: From February to April 2020, a retrospective analysis investigated COVID-19 patients treated via Korean telemedicine. The study involved asymptomatic to mild cases receiving Cheongpebaedok-tang more than three times, along with continuous Korean medicine care in convalescence. Diagnoses and treatment adhered to the telemedicine guidelines of the Association of Korean Medicine, with varied Cheongpebaedok-tang prescriptions based on symptom severity. Symptom evaluation involved a detailed assessment using a 15-item tool at initial and final sessions.Results: The study included 27 patients, with a mean age of 48.7 ± 2.3 years (mean ± standard error). Patients began self-administering oral Cheongpebaedok-tang for an average of 19.4 ± 1.8 days after the date of COVID-19 diagnosis confirmation and continued the medication for 15.8 ± 1.2 days. The reported side effects of the Cheongpebaedok-tang included palpitations (11.1%), insomnia (7.4%), dizziness (3.7%), and diarrhea (3.7%). All side effects disappeared after adjusting the prescription according to standard treatment guidelines. The occurrence of all COVID-19-related adverse symptoms, except fatigue and myalgia, decreased. Fatigue was the most common chronic symptom persisting after 6 months (51.9%), followed by ocular symptoms (37.0%) and sore throat (22.2%).Conclusions: This study implies Cheongpebaedok-tang may offer a potentially safe, symptom-alleviating approach for managing mild COVID-19 cases via telemedicine, although further comprehensive research is warranted.

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“…Therefore, it is of great practical significance to classify and identify tongue color and moss color. The color of the tongue is generally divided into five kinds: light red, light white, red crimson, green, and purple (Figure 10) (Chen et al, 2022;Oji et al, 2014). The color of tongue coating is generally divided into three kinds: white, yellow, and gray-black (Figure 10).…”

Section: Analysis Of Tongue Image Featuresmentioning

confidence: 99%

Artificial Intelligence in Tongue Image Recognition

Chu,

Ji,

Zhu

et al. 2023

International Journal of Software Science and Computational Intelligence

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Tongue image recognition is a traditional Chinese medicine diagnosis method, which uses the shape, color, and texture of the tongue to judge the health of the human body. With the rapid development of artificial intelligence technology, the application of artificial intelligence in the field of tongue recognition has been widely considered. Based on the intelligent analysis of tongue diagnosis in traditional Chinese medicine, this paper reviews the application progress of artificial intelligence in tongue image recognition in recent years and analyzes its potential and challenges in this field. Firstly, this paper introduces three steps of tongue image recognition, including tongue image acquisition, tongue image preprocessing, and tongue image feature analysis. The application of traditional methods and artificial intelligence methods in the whole process of tongue image recognition is reviewed, especially the tongue body segmentation, and the advantages and disadvantages of convolutional neural networks are analyzed and compared. Artificial intelligence can use technologies such as deep learning and computer vision to automatically analyze and extract features from tongue images. By constructing a tongue image recognition model, tongue shape, color, texture, and other features can be accurately recognized and quantitatively analyzed. Finally, this paper summarizes the problems existing in artificial intelligence in tongue image recognition and looks forward to the future developmental direction of this field. It can promote the modernization of TCM diagnostic methods, achieve early disease screening and prevention, personalized medicine and treatment optimization, and support medical research and knowledge accumulation. However, there is still a need for further validation and practice, with a focus on patient privacy and data security.

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Computational tongue color simulation in tongue diagnosis

Cited by 6 publications

References 22 publications

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Management of asymptomatic to mild COVID-19 patients with Cheongpebaedok-tang on the telemedical basis: A retrospective observational case series

Artificial Intelligence in Tongue Image Recognition

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