A multicenter diagnostic study of thyroid nodule with Hashimoto's thyroiditis enabled by Hashimoto's thyroiditis nodule-artificial intelligence model.

OBJECTIVE

This study aimed to develop a Hashimoto's thyroiditis nodule-artificial intelligence (HTN-AI) model to optimize the diagnosis of thyroid nodules with Hashimoto's thyroiditis (HT) of which the efficiency and accuracy remain challenging.

DESIGN AND METHODS

This study included 5709 patients from 10 hospitals between January 2014 and March 2024. Among them, 5053 thyroid nodules were divided into training and testing sets in a 9:1 ratio. Then, we tested the model on an external dataset (n = 432). Finally, we prospectively recruited 224 patients with dynamic ultrasound videos acquired and employed the HTN-AI model to identify nodules from the dynamic ultrasound videos. Radiologists of varying seniority performed the categorization of thyroid nodules as benign and malignant, both with and without the assistance of the HTN-AI model, and their diagnostic performances were compared.

RESULTS

The results indicated that for the external testing set, the HTN-AI model achieved a Dice similarity coefficient (DSC) of 0.91, outperforming several other common convolutional neural network (CNN) models. Specifically, the DSCs of the HTN-AI model were similar for thyroid nodule patients with and without HT which were 0.91 ± 0.06 and 0.91 ± 0.09. Moreover, when the HTN-AI model was used to assist diagnosis, it demonstrated an improvement in the diagnostic performance of radiologists. The diagnostic areas under the receiver operating characteristic curve (AUCs) of the junior radiologists increased from 0.59, 0.59, and 0.57 to 0.68, 0.65, and 0.65.

CONCLUSIONS

This research demonstrates that the HTN-AI model has excellent performance in identifying thyroid nodules associated with HT and can assist radiologists with more accurate and efficient diagnoses of thyroid nodules.

KEY POINTS

Question The study developed an HTN-AI model aimed at assisting in the diagnosis of thyroid nodules in patients with HT. Findings The HTN-AI model achieved great performance with a Dice similarity coefficient (DSC) of 0.91, and consistent performance across patients with and without HT. Clinical relevance The HTN-AI model enhances the accuracy and efficiency of thyroid nodule diagnosis, particularly in patients with HT. By assisting radiologists at varying experience levels, this model supports improved decision-making in the management of thyroid nodules.