To explore the impact of artificial intelligence (AI) ultrasound and quality control circle (QCC) activity on junior sonographers' ability to accurately estimate the risk of thyroid nodules.

From January to October 2019, 119 two-dimensional ultrasonographic images of thyroid nodules with surgical pathology outcomes were collected in our hospital. AI ultrasound in conjunction with QCC activity was used to continually enhance two junior physicians' (doctors 1 and 2) ability to assess thyroid nodule risk. The gold standard for diagnosing benign and malignant thyroid nodules was surgical pathology. The signs of thyroid nodules recognized by two senior physicians were used as the gold standard. Two junior sonographers used the 2017 American College of Radiology Thyroid Imaging Reporting and Data System Thyroid Nodules (ACR TI-RADS) to assess thyroid nodules both before and after the activity. Then, both before and after the activity, whether thyroid ultrasound examination procedures were standard, the qualified rate of image storage, and patients' trust in younger physicians were examined. Receiver operating characteristic (ROC) curves for benign and malignant thyroid nodules were plotted before and after the activity, and the DeLong test was used to compare the difference in diagnostic efficiency between the two junior sonographers. The McNemar test was used to compare the recognition accuracy of the two junior sonographers (doctor 1 and doctor 2) for thyroid nodule ultrasound signs before and after the activity.

The two young doctors' ability to identify the echo of thyroid nodules was increased by the use of AI ultrasound (doctor 1: 47.90% vs 53.78%, P=0.031; doctor 2: 45.38% vs 53.78%, P=0.004). Doctor 2's ability to recognize thyroid nodules' component (69.75% vs 80.67%, P=0.004), shape (58.82% vs 63.87%, P=0.021), and punctate hyperechogenicity (52.10% vs 56.30%, P=0.031) was similarly enhanced at the same time. The AUC values of the two junior doctors for diagnosing thyroid nodules were significantly higher after QCC activity than before (doctor 1: 0.878 vs 0.921, P=0.036; doctor 2: 0.824 vs 0.883, P=0.001). The qualified rate of image storage grew from 50% to 90%, the qualified rate of thyroid ultrasound examination by junior physicians improved from 60% to 95%, and the patients' trust in junior physicians increased from 70% to 90% as a result of QCC activity.

AI ultrasound combined with QCC activity can improve the ability of junior sonographers to assess the malignant risk of thyroid nodules in an all-round and multi-dimensional way, and improve the quality of medical services.