ArticleHeliyon2024
Application of intelligent tongue image analysis in Conjunction with microbiomes in the diagnosis of MAFLD.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Artificial intelligence-based tongue image analysis for non-invasive screening of gastric cancer, precancerous lesions, and related gastrointestinal disorders: a systematic review.Frontiers in medicine · 2026Pooled it
- Reframing glucolipid metabolic disorders through the lens of the oral microbiome: from pathophysiological mechanisms to translational potential.Frontiers in nutrition · 2026Review
- Integrated multi-omics analysis unveils microbiota-metabolite-host interactions and novel biomarkers for early diabetic kidney disease diagnosis.Frontiers in immunology · 2026Article
- Correlation analysis between tongue hyperspectral imaging and tongue coating microbiota and feasibility study for early screening of type 2 diabetes mellitus status.Frontiers in endocrinology · 2026Article
- Beyond biomarkers: An integrated traditional Chinese medicine-machine learning approach predicts hepatic steatosis in high metabolic risk populations.World journal of gastroenterology · 2025Article
- Article
- Machine learning-based identification of biochemical markers to predict hepatic steatosis in patients at high metabolic risk.World journal of gastroenterology · 2025Article
- Clinical study of intelligent tongue diagnosis and oral microbiome for classifying TCM syndromes in MASLD.Chinese medicine · 2025Article
- Emerging technologies for early risk stratification and precision management of diabetic kidney disease: a multimodal framework integrating digital phenotypes and clinical biomarkers.Frontiers in endocrinology · 2025Review
- Deep learning approach for objective differentiation of kidney deficiency syndrome in reproductive age females: a tongue-face fusion model.Frontiers in physiology · 2025Article
- Generative AI: A transformative force in advancing research and care in metabolic dysfunction-associated fatty liver disease.Liver research (Beijing, China) · 2024Article
- Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.Frontiers in cellular and infection microbiology · 2024Article
- Feasibility of tongue image detection for coronary artery disease: based on deep learning.Frontiers in cardiovascular medicine · 2024Article
- Intelligent tongue and facial image analysis for noninvasive prediction of glucolipid metabolic disorders.Digital healthArticle
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Metabolic associated fatty liver disease (MAFLD) is a widespread liver disease that can lead to liver fibrosis and cirrhosis. Therefore, it is essential to develop early diagnosic and screening methods. Methods: We performed a cross-sectional observational study. In this study, based on data from 92 patients with MAFLD and 74 healthy individuals, we observed the characteristics of tongue images, tongue coating and intestinal flora. A generative adversarial network was used to extract tongue image features, and 16S rRNA sequencing was performed using the tongue coating and intestinal flora. We then applied tongue image analysis technology combined with microbiome technology to obtain an MAFLD early screening model with higher accuracy. In addition, we compared different modelling methods, including Extreme Gradient Boosting (XGBoost), random forest, neural networks(MLP), stochastic gradient descent(SGD), and support vector machine(SVM). Results: The results show that tongue-coating Streptococcus and Rothia, intestinal Blautia, and Streptococcus are potential biomarkers for MAFLD. The diagnostic model jointly incorporating tongue image features, basic information (gender, age, BMI), and tongue coating marker flora (Streptococcus, Rothia), can have an accuracy of 96.39%, higher than the accuracy value except for bacteria. Conclusion: Combining computer-intelligent tongue diagnosis with microbiome technology enhances MAFLD diagnostic accuracy and provides a convenient early screening reference.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.