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

ObjectiveThe aim of this study is 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.MethodsThis was a retrospective study. The labial gland pathological sections of total 121 CTD patients were collected.

According to the results of thyroid autoantibodies, including thyroglobulin antibody (TgAb) and thyroid peroxidase antibody (TPOAb), the patients were divided into positive group (Ab+ Group) and negative group (Ab- Group). The pre-trained model EfficientNet-B5 was used to extract image features, and combined with multi-instance learning and ensemble learning techniques, the high-risk prediction model for CTD patients with AIT was constructed.ResultThe integrated model showed excellent prediction performance in both the internal validation set and the external validation set, with the area under the receiver operating characteristic curve (AUC) of 0.829.