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

Recruiting

• Conditions: Thyroid Nodule
• Interventions: Other: Artificial Intelligent

• Registration Number: NCT05982821
• Lead Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Brief 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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2023-07-24
• Status Verified: 2023-08
• Study First Submitted QC: 2023-08-08
• Last Update Submitted: 2023-08-08
• Study First Posted: 2023-08-09
• Last Update Posted: 2023-08-09
• Study Start: 2024-01-03
• Primary Completion: 2026-07-30
• Study Completion: 2026-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Training set	Artificial Intelligent	Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2018 and December 2020 in Sun Yat-sen Memorial Hospital Sun Yat-sen University.
Internal test set	Artificial Intelligent	Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2021 and May 2023 in Sun Yat-sen Memorial Hospital Sun Yat-sen University.
External test set	Artificial Intelligent	Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2022 and June 2023 in Houjie Hospital of Dongguan and Central People's Hospital of Zhanjiang.

Primary Outcome Measures

Name	Time	Method
The cases time.	At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.	The faculty responsible for the training program assessed the skills of each resident.
Area under curve.	At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.	Receiver operating characteristic curve analysis.
The number of cases	At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.	The faculty responsible for the training program assessed the skills of each resident.

Trial Locations

Locations (1)

Sun Yat-sen Memorial Hospital, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China