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Currently, there is a lack of adequate methods to assess insomnia objectively. This study addresses the usefulness of tongue features and oral microbial profile as a potential diagnostic biomarker of insomnia. One hundred insomniac patients and 20 healthy control subjects were selected. Their demographic and clinical characteristics, as well as the tongue diagnostic indices and oral microbial profile, were examined. Compared to the control group, insomniac patients showed a higher abnormal low-frequency/high-frequency (LF/HF) ratio. In tongue diagnosis, the indices related to lightness of tongue body and tongue coating were higher in the insomniac group vs. the control group. Furthermore, linear discriminant analysis (LDA) of oral microbial population revealed that the relative abundances of Clostridia, Veillonella, Bacillus and Lachnospiraceae were significantly higher in the insomniac patients than the control group. Additionally, the tongue features of the insomniac group exhibited that the non-coating group had a poor sleep condition compared to the thick-coating group, although the difference was insignificant. On the other hand, the oral microbial communities of the insomniac patients revealed greater alpha and beta diversities in the non-coating group vs. the thick-coating group. The alpha and beta diversities were higher in orotype1 than orotype2. Collectively, this study highlighted that the lightness of tongue body and tongue coating as well as oral microbial profiles of SR1, Actinobacteria, Clostridia and Lachnospiraceae_unclassified could be considered potential biomarkers of insomnia.

Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep learning (DL), specifically in computer vision (CV) and natural language processing (NLP), offer novel opportunities to modernize TCM by exploring the profound connotations of its theory. This review begins with an overview of the ML and DL methods employed in TCM; this is followed by practical instances of these applications. Furthermore, extensive discussions emphasize the mature integration of ML and DL in TCM, such as tongue diagnosis, pulse diagnosis, and syndrome differentiation treatment, highlighting their early successful application in the TCM field. Finally, this study validates the accomplishments and addresses the problems and challenges posed by the application and development of TCM powered by ML and DL. As ML and DL techniques continue to evolve, modern technology will spark new advances in TCM.

Tongue crack is an important pathological feature in traditional Chinese tongue diagnosis. However, in computerized tongue diagnosis, tongue cracks are not commonly used because the tongue crack features are hard to be accurately extracted. In this paper, we propose a new scheme for tongue crack extraction. First, for the purpose of enhancement of the tongue crack regions, we get line response image using tongue image gray-level and color information as well as the pixel distant gradient. Then tongue crack regions are extracted from line response image by methods such as hysteresis thresholding algorithm and so on. A database of 286 tongue images is established and used in our test, and the experimental results demonstrate the effectiveness of our proposed tongue crack detection method.

This article reports an application of using Gabor Wavelet Opponent Colour Features (GWOCF) for tongue diagnosis in Traditional Chinese Medicine (TCM). This project focuses on the standardization of the tongue diagnosis in TCM. The objective is to develop a set of quantitative measurement for tongue diagnosis using computer vision techniques. To achieve this goal, we propose to use GWOCF for determining different types of tongue proper and tongue coating textures from a tongue image. Two experiments are presented. In the first experiment, GWOCF is directly extracted from tongue image for recognition. In the second experiment, we employ colour information to pre-classify the known texture image before extracting GWOCF. 63 tongue images captured from 63 patients in Guangzhou University of Traditional Chinese Medicine's hospital are used for testing. It is found that with colour pre-classification process, 89% of recognition accuracy can be achieved. This is a revised version of our conference paper in [12].

This paper deals with software development for promoting the TCM tongue diagnosis’s objectification and standardization through tongue diagnosis assist system based on web service. Web service provider is used to provide access to TCM tongue diagnosis terminology for the system client and third application. The TCM tongue diagnosis assist system will include multi-platform client, the management client and the web service. This system can not only bring convenience to TCM staff, but also promote the standardization of TCM tongue diagnosis terminology for TCM electronic medical record, and will simplify statistical analysis work of tongue diagnosis data in the future.