Clinical study of intelligent tongue diagnosis and oral microbiome for classifying TCM syndromes in MASLD.

Abstract

Background: This study aimed to analyze the tongue image features and oral microbial markers in different TCM syndromes related to metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods: This study involved 34 healthy volunteers and 66 MASLD patients [36 with Dampness-Heat (DH) and 30 with Qi-Deficiency (QD) syndrome]. Oral microbiome analysis was conducted through 16S rRNA sequencing. Tongue image feature extraction used the Uncertainty Augmented Context Attention Network (UACANet), while syndrome classification was performed using five different machine learning methods based on tongue features and oral microbiota.

Results: Significant differences in tongue color, coating, and oral microbiota were noted between DH band QD syndromes in MASLD patients. DH patients exhibited a red-crimson tongue color with a greasy coating and enriched Streptococcus and Rothia on the tongue. In contrast, QD patients displayed a pale tongue with higher abundances of Neisseria, Fusobacterium, Porphyromonas and Haemophilus. Combining tongue image characteristics with oral microbiota differentiated DH and QD syndromes with an AUC of 0.939 and an accuracy of 85%.

Conclusion: This study suggests that tongue characteristics are related to microbial metabolism, and different MASLD syndromes possess distinct biomarkers, supporting syndrome classification.