Artificial Intelligent Accelerates the Learning Curve for Mastering Contrast-enhanced Ultrasound of Thyroid Nodules
Study Details
Study Description
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:
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Can we develop a artificial intelligent software to assist doctors in the diagnosis of thyroid nodules using contrast-enhanced ultrasound?
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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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Training set 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. |
Other: Artificial Intelligent
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.
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| Internal test set 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. |
Other: Artificial Intelligent
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.
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| External test set 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. |
Other: Artificial Intelligent
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.
|
Outcome Measures
Primary Outcome Measures
- 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.
- 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.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Patients with thyroid nodules with a solid component ≥5 mm confirmed by conventional ultrasound;
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Patients who underwent conventional ultrasound, contrast-enhanced ultrasound, and fine-needle aspiration biopsy;
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Patients with a final benign or malignant pathological results.
Exclusion Criteria:
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Patients with cytopathology of Bethesda I, III, or IV and without final benign or malignant pathology；
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Patients with a history of thyroid ablation or surgery;
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Patients with low-quality ultrasound images.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Sun Yat-sen Memorial Hospital, Sun Yat-sen University | Guangzhou | Guangdong | China | 510289 |
Sponsors and Collaborators
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Investigators
- Principal Investigator: Jingliang Ruan, PhD, Sun Yat-sen Memorial Hospital,Sun Yat-sen University
Study Documents (Full-Text)
None provided.More Information
Publications
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- Zhou J, Yin L, Wei X, Zhang S, Song Y, Luo B, Li J, Qian L, Cui L, Chen W, Wen C, Peng Y, Chen Q, Lu M, Chen M, Wu R, Zhou W, Xue E, Li Y, Yang L, Mi C, Zhang R, Wu G, Du G, Huang D, Zhan W; Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of the Chinese Medical Association; Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. 2020 Chinese guidelines for ultrasound malignancy risk stratification of thyroid nodules: the C-TIRADS. Endocrine. 2020 Nov;70(2):256-279. doi: 10.1007/s12020-020-02441-y. Epub 2020 Aug 21.
- SYSKY-2023-702-01