Abstract
OBJECTIVES: Ultrasound examination is a primary method for detecting thyroid lesions in clinical practice. Incorrect ultrasound diagnosis may lead to delayed treatment or unnecessary biopsy punctures. Therefore, our objective is to propose an artificial intelligence model to increase the precision of thyroid ultrasound diagnosis and reduce puncture rates. METHODS: We consecutively collected ultrasound recordings from 672 patients with 845 nodules across two Chinese hospitals. This dataset was divided into training, validation, and internal test sets in a ratio of 7:1:2. We constructed and tested six different model variants based on different video feature distillation strategies and whether additional information from ROI (Region of Interest) scales was used. The models' performances were evaluated using the internal test set and an additional external test set containing 126 nodules from a third hospital. RESULTS: The dual-stream model, which contains both raw-scale and ROI-scale streams with the time dimensional convolution layer, achieved the best performance on both internal and external test sets. On the internal test set, it achieved an AUROC (Area Under Receiver Operating Characteristic Curve) of 0.969 (95 % confidence interval, CI: 0.944-0.993) and an accuracy of 92.6 %, outperforming other variants (AUROC: 0.936-0.955, accuracy: 80.2%-88.3 %) and experienced radiologists (accuracy: 91.9 %). The AUROC of the best model in the external test was 0.931 (95 % CI: 0.890-0.972). CONCLUSION: Integrating a dual-stream model with additional ROI scale information and the time dimensional convolution layer can improve performance in diagnosing thyroid ultrasound videos.