Artificial Intelligent Accelerates the Learning Curve for Mastering Contrast-enhanced Ultrasound of Thyroid Nodules
NCT ID: NCT05982821
Last Updated: 2023-08-09
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
1000 participants
OBSERVATIONAL
2024-01-03
2026-12-31
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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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.
Artificial Intelligent
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.
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.
Artificial Intelligent
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.
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.
Artificial Intelligent
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.
Interventions
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Artificial Intelligent
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.
Eligibility Criteria
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Inclusion Criteria
* 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 a history of thyroid ablation or surgery;
* Patients with low-quality ultrasound images.
18 Years
ALL
No
Sponsors
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Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
OTHER
Responsible Party
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Principal Investigators
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Jingliang Ruan, PhD
Role: PRINCIPAL_INVESTIGATOR
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Locations
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Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Guangzhou, Guangdong, China
Countries
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Central Contacts
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Facility Contacts
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References
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Other Identifiers
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SYSKY-2023-702-01
Identifier Type: -
Identifier Source: org_study_id
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