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

Thyroid Nodule Clinical Trial

Official title:

Artificial Intelligent Accelerates the Learning Curve for Mastering Thyroid Imaging Reporting and Data System of Contrast-enhanced Ultrasound

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05982821
• Other study ID #: SYSKY-2023-702-01
• Status: Recruiting
• Start date: January 3, 2024
• Est. completion date: December 31, 2026

Study information

• Verified date: August 2023
• Source: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
• Contact: Jingliang Ruan, PhD
• Phone: +8613694202230
• Email: ruanjl3[@]mail.sysu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: December 31, 2026
• Est. primary completion date: July 30, 2026
• Gender: All
• Age group: 18 Years and older

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.

Related Conditions & MeSH terms

• Thyroid Diseases
• Thyroid Nodule

Intervention

Other:
• Artificial Intelligent
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.

Locations

Country	Name	City	State
China	Sun Yat-sen Memorial Hospital, Sun Yat-sen University	Guangzhou	Guangdong

Sponsors (1)

Lead Sponsor	Collaborator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Country where clinical trial is conducted

China, 

References & Publications (19)

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Chen Y, Gao Z, He Y, Mai W, Li J, Zhou M, Li S, Yi W, Wu S, Bai T, Zhang N, Zeng W, Lu Y, Liu H. An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules. Radiology. 2022 Jun;303(3):613-619. doi: 10.1148/radiol.211455. Epub 2022 Mar 22. — View Citation

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Seifert P, Gorges R, Zimny M, Kreissl MC, Schenke S. Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules. Endocrine. 2020 Jan;67(1):143-154. doi: 10.1007/s12020-019-02134-1. Epub 2019 Nov 18. — View Citation

Shin JH, Baek JH, Chung J, Ha EJ, Kim JH, Lee YH, Lim HK, Moon WJ, Na DG, Park JS, Choi YJ, Hahn SY, Jeon SJ, Jung SL, Kim DW, Kim EK, Kwak JY, Lee CY, Lee HJ, Lee JH, Lee JH, Lee KH, Park SW, Sung JY; Korean Society of Thyroid Radiology (KSThR) and Korean Society of Radiology. Ultrasonography Diagnosis and Imaging-Based Management of Thyroid Nodules: Revised Korean Society of Thyroid Radiology Consensus Statement and Recommendations. Korean J Radiol. 2016 May-Jun;17(3):370-95. doi: 10.3348/kjr.2016.17.3.370. Epub 2016 Apr 14. — View Citation

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Wildman-Tobriner B, Buda M, Hoang JK, Middleton WD, Thayer D, Short RG, Tessler FN, Mazurowski MA. Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. Radiology. 2019 Jul;292(1):112-119. doi: 10.1148/radiol.2019182128. Epub 2019 May 21. — View Citation

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Zhang Y, Zhou P, Tian SM, Zhao YF, Li JL, Li L. Usefulness of combined use of contrast-enhanced ultrasound and TI-RADS classification for the differentiation of benign from malignant lesions of thyroid nodules. Eur Radiol. 2017 Apr;27(4):1527-1536. doi: 10.1007/s00330-016-4508-y. Epub 2016 Aug 15. — View Citation

Zhao CK, Ren TT, Yin YF, Shi H, Wang HX, Zhou BY, Wang XR, Li X, Zhang YF, Liu C, Xu HX. A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate. Thyroid. 2021 Mar;31(3):470-481. doi: 10.1089/thy.2020.0305. Epub 2020 Sep 9. — View Citation

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* Note: There are 19 references in all — Click here to view all references

Outcome

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