Objective:

To construct a deep learning-based prediction model to accurately predict the risk of autoimmune thyroiditis (AIT) in patients with connective tissue disease (CTD) using whole section images (WSI) of labial gland pathological tissue.

Approach:

Key Findings:

• The integrated model demonstrated excellent prediction performance with an AUC of 0.829 in both internal and external validation sets.
• The model effectively identified key pathological features of labial gland tissues associated with high risk of AIT in CTD patients.

Interpretation:

The study confirmed the efficacy of a deep learning-based prediction model in evaluating the risk of AIT in CTD patients.

Limitations:

• The study is retrospective and may be subject to biases inherent in such designs.
• The sample size of 121 patients may limit the generalizability of the findings.

Conclusion:

The prediction model based on labial gland WSI using deep learning shows promise for assessing AIT risk in CTD patients.

Source

Based on findings from:

Labial-gland artificial intelligence model screening for autoimmune thyroiditis among patients with connective tissue disease

Jia-yun Wu, Yuening Kang, Xiao-min Li, Wen-qi Xia, Ru-yi Liao, Zhi-yang He, Yu-ling Chen, Ya Wen, Fan-xuan Meng, Jing-yu Zhang, Zheng Yang, Yong Ren, Qing Lv. Frontiers In Immunology, 2026.

https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1861419/full

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