Tongue Image Analysis for Diabetes Mellitus Diagnosis Based on SOM Kohonen

Tongue diagnosis is an important diagnostic method for evaluating the condition of internal organ by looking at the image of tongue . However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, SOM Kohonen are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on 35 patients affected by Diabetes Mellitus as well as other 30 healthy volunteers, and the diagnostic results predicted by the previously trained SOM Kohonen classifiers are compared with the HOMA-B

Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

Combining Artificial Intelligence with Traditional Chinese Medicine for Intelligent Health Management

The growth of artificial intelligence (AI) is being referred to as the beginning of "the fourth industrial revolution". With the rapid development of hardware, algorithms, and applications, AI not only provides a new concept and relevant solutions to solve the problem of complexity science but also provides a new concept and method to promote the development of traditional Chinese medicine (TCM). In this study, based on the research and development of AI technology applications in biomedical and clinical diagnosis and treatment, we introduce AI technologies in current TCM research. This can have applications in intelligent clinical information acquisition, intelligent clinical decision, and efficacy evaluation of TCM; intelligent classification management, intelligent prescription, and drug research in Chinese herbal medicine; and health management. Furthermore, we propose a framework of "intelligent TCM" and outline its development prospects

Comparative Analysis of Tongue Indices between Patients with and without a Self-Reported Yin Deficiency: A Cross-Sectional Study

We investigated the hypothesis that Yin-deficient patients have a reddened tongue with less coating. We screened 189 participants aged 20 to 49 years, complaining of headache. To classify patients in terms of Yin deficiency, we used two self-reporting Yin-deficiency questionnaires (Yin-Deficiency Questionnaire and Yin-Deficiency Scale) and diagnosis by a doctor. Based on the tests, a total of 33 subjects were assigned to a Yin-deficient group and 33 subjects were assigned to a nondeficient control group. Tongue images were acquired using a computerized tongue diagnostic system, for evaluating tongue indices. The tongue coating percentage and tongue redness were calculated as the mean a⁎ value of both the whole tongue area (WT a⁎) and the tongue body area (TB a⁎). The tongue coating percentage of the Yin-deficient group (34.79 ± 10.76) was lower than that of the nondeficient group (44.13 ± 14.08). The WT a⁎ value of the Yin-deficient group (19.39 ± 1.52) was significantly higher than that of the nondeficient group (18.21 ± 2.06). However, the difference in the TB a⁎ value between the two groups was not significant. In conclusion, we verified that Yin-deficient patients had less tongue coating and tended to have a more reddish tongue than nondeficient patients