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

By
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
June 23, 2026
0 min

Frontiers In Immunology

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.

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

Objective:

Approach:

Key Findings:

Interpretation:

Limitations:

Conclusion:

Original Source(s)

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

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