Artificial Intelligent Accelerates the Learning Curve for Mastering Contrast-enhanced Ultrasound of Thyroid Nodules

Summary

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.

Conditions2

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