A Clinical Study on the Relationship Among Insomnia, Tongue Diagnosis, and Oral Microbiome

Park, Seohyun; Shin, Na Rae; Yang, Miying; Bose, Shambhunath; Kwon, Ojin; Nam, Dong Hyun; Lee, Jun-Hwan; Song, Eun-Ji; Nam, Young-Do; Kim, Hojun

doi:10.1142/s0192415x2250032x

Cited by 3 publications

(3 citation statements)

References 67 publications

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“…Compared to the control group, insomniac patients exhibited a significantly higher abnormal low-frequency/high-frequency (LF/HF) ratio. Furthermore, linear discriminant analysis 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 (Park et al, 2022). The impact of various lifestyle and environmental factors on sleep is well-documented.…”

Section: Discussionmentioning

confidence: 99%

Efficacy and safety of fecal microbiota transplantation for chronic insomnia in adults: a real world study

Fang,

Yao,

et al. 2023

Front. Microbiol.

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ObjectiveTo assess the efficacy and safety of fecal microbiota transplantation (FMT) for adult chronic insomnia.MethodsPatients treated with FMT for chronic diseases were divided into chronic insomnia and non-insomnia group. The primary endpoint was the efficacy of FMT for insomnia 4 weeks after treatment, the secondary endpoints included the impacts of FMT on anxiety, depression, health-related quality of life, gut microbiota, and adverse events associated with FMT. Insomnia Severity Index (ISI) and Pittsburgh Sleep Quality Index (PSQI) were utilized to assess the efficacy of FMT on insomnia, self-rating anxiety/depression scale [Zung Self-Rating Anxiety Scale (SAS), Zung Self-Rating Depression Scale (SDS)] was employed to evaluate anxiety and depression. Quality of life was evaluated by SF-36. 16S rRNA sequencing was employed to analyze the gut microbiota and correlation analysis was performed.ResultsForty patients met the inclusion criteria and seven were excluded. 33 patients were enrolled and stratified into chronic insomnia group (N = 17) and non-insomnia group (N = 16). Compared to baseline, FMT significantly ameliorated the ISI (17.31 ± 5.12 vs. 5.38 ± 5.99), PSQI (14.56 ± 2.13 vs. 6.63 ± 4.67), SAS (54.25 ± 8.90 vs. 43.68 ± 10.64) and SDS (57.43 ± 10.96 vs. 50.68 ± 15.27) score and quality of life of chronic insomnia patients. 76.47% (13/17) of insomnia patients achieved the primary endpoints. In chronic insomnia patients, the relative abundance of Eggerthella marked enhanced at baseline, while the relative abundance of Lactobacillus, Bifidobacterium, Turicibacter, Anaerostipes, and Eisenbergiella significantly increased after FMT treatment, the latter positive correlated with the efficacy of FMT. Encouragingly, FMT also improved the sleep quality of non-insomnia patients.ConclusionEggerthella may potentially serve as a distinctive genus associated with chronic insomnia. FMT maybe a novel treatment option for adults with chronic insomnia and provide an alternative to traditional treatments for insomnia. The effects were positive correlated with the augmentation of probiotics, such as Bifidobacterium, Lactobacillus, Turicibacter, and Fusobacterium.

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Section: Discussionmentioning

confidence: 99%

Efficacy and safety of fecal microbiota transplantation for chronic insomnia in adults: a real world study

Fang,

Yao,

et al. 2023

Front. Microbiol.

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“…With the rapid development of society and the continuous collision between tradition and modern science, more and more tongue diagnostic researchers full of curiosity have carried out active exploration. Many studies have shown that the external manifestations of the tongue are significantly related to some diseases and health indicators in the body 3 , 27 – 30 and even COVID-19. 4 , 31 – 33 Meanwhile, the movement of the tongue also relies on the function of the brain 34 , 35 and can be used for identity verification.…”

Section: Tongue Diagnosis In Tcm (Background)mentioning

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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“…Tongue diagnosis based on a machine learning algorithm has been applied to many diseases, such as diabetes (Fan et al, 2021), stomach trouble (Yuan et al, 2023), appendicitis (Pang et al, 2005), etc. (Chung et al, 2022;Lee et al, 2016a;Lo et al, 2015;Park et al, 2022;Wu et al, 2018). Jiang et al (2012) demonstrated that visual signatures of the microbiome in tongue coatings reflect health status, specifically GI disorders.…”

Section: Introductionmentioning

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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A Clinical Study on the Relationship Among Insomnia, Tongue Diagnosis, and Oral Microbiome

Cited by 3 publications

References 67 publications

Efficacy and safety of fecal microbiota transplantation for chronic insomnia in adults: a real world study

Efficacy and safety of fecal microbiota transplantation for chronic insomnia in adults: a real world study

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

Artificial Intelligence in Tongue Image Recognition

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