Overview

The goal of this observational study is to learn about the learning curve for mastering the thyroid imaging reporting and data system of contrast-enhanced ultrasound with the assistance of artificial intelligence in patients with thyroid nodules. The main questions it aims to answer are:

1. Can we develop a artificial intelligent software to assist doctors in the diagnosis of thyroid nodules using contrast-enhanced ultrasound?
2. Can artificial intelligent reduce the number of cases and time for doctors to master the contrast-enhanced ultrasound diagnosis of thyroid nodules?

Participants will be asked to undergo contrast-enhanced ultrasound examination and ultrasound-guided fine-needle aspiration of thyroid nodules. Researchers will compare the number of cases and time for doctors with and without artificial intelligent assistance to master the contrast-enhanced ultrasound diagnosis of thyroid nodules to see if artificial intelligent reduce the number of cases and time.

Eligibility

Inclusion Criteria:

• Patients with thyroid nodules with a solid component ≥5 mm confirmed by conventional ultrasound;
• Patients who underwent conventional ultrasound, contrast-enhanced ultrasound, and fine-needle aspiration biopsy;
• Patients with a final benign or malignant pathological results.

Exclusion Criteria:

• Patients with cytopathology of Bethesda I, III, or IV and without final benign or malignant pathology；
• Patients with a history of thyroid ablation or surgery;
• Patients with low-quality ultrasound images.