ORIGINAL RESEARCH article
Front. Cell. Infect. Microbiol.
Sec. Oral Microbes and Host
Volume 15 - 2025 | doi: 10.3389/fcimb.2025.1602883
This article is part of the Research TopicWomen in Oral Microbes & Host: 2025View all articles
Diagnosis of Non-Puerperal Mastitis Based on "Whole Tongue" Features: Non-invasive Biomarker Mining and Diagnostic Model Construction
Provisionally accepted
- 1Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- 2Department of Traditional Chinese Medicine Breast, Longhua Hospital, Shanghai, China
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Background Non-puerperal mastitis (NPM) arises from heterogeneous factors ranging from autoimmune dysregulation to occult infections. To establish a diagnosis, biopsy is reliable but invasive. Imaging exhibits a limited specificity and may cause diagnostic delays, patient discomfort, and suboptimal management. Inspired by non-invasive tongue diagnosis in traditional Chinese medicine, this study integrated tongue-coating microbiota profiling and AI-quantified tongue image phenotyping to establish an objective, non-invasive diagnostic framework for NPM.Methods A total of 100 NPM patients from the Breast Surgery Department of Longhua Hospital and 100 healthy volunteers were included. Their clinical characteristics, tongue images, and tongue-coating microbiota data were collected. Features of tongue images (detection, segmentation, classification) were quantitated and extracted via deep learning. Microbiota composition was assessed using 16S rRNA gene sequencing (V3-V4 region) and bioinformatic pipelines (QIIME2, DADA2). Based on clinical, imaging and microbial features, three machine learning modelslogistic regression (LR), support vector machine (SVM), and gradient boosting decision tree (GBDT)were trained to distinguish NPM.The GBDT model achieved a superior diagnostic performance (AUROC=0.98, accuracy=0.95, specificity=0.95), outperforming the LR (AUROC=0.98, accuracy=0.95, specificity=0.90) and SVM models (AUROC=0.87, accuracy=0.80, specificity=0.75).Integration of clinical characteristics, tongue image features, and bacterial profiles (at the genus/family level) yielded the highest accuracy, whereas models using a single class of features showed a lower discriminatory ability (AUROC=0.90-0.91). Key predictors included Campylobacter (12%), waist-hip ratio (11%), and Alloprevotella (6%).Integrating clinical characteristics, tongue image features and tongue-3 coating microbiota profiles, the multimodal GBDT model demonstrates a high diagnostic accuracy, supporting its utility for early screening and diagnosis of NPM.
Keywords: Non-puerperal mastitis, Tongue diagnosis, tongue microbiota, highthroughput sequencing, Machine learning model
Received: 30 Mar 2025; Accepted: 25 Jun 2025.
Copyright: © 2025 Tu, Yin, Ma, Chen and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hongfeng Chen, Department of Traditional Chinese Medicine Breast, Longhua Hospital, Shanghai, China
Meina Ye, Department of Traditional Chinese Medicine Breast, Longhua Hospital, Shanghai, China
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