NCT05381064|Unknown
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Sponsor study identifier
interventional
184
0 countries
N/A
Study Start
June 1, 2022
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Primary Completion
December 1, 2023
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Study Completion
January 1, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
The bile duct scanning system based on deep learning can prompt endoscopists to scan standard stations and identify bile ducts and stones in real time. The purpose of this study is to evaluate the effectiveness and safety of the proposed deep learning-based bile duct scanning system in improving the diagnostic accuracy of common bile duct stones and reducing the rate of missed gallstones during bile duct scanning by novice ultrasound endoscopists in a single-center, tandem, randomized controlled trial
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
January 1, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
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Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
May 13, 2022
Actual (reported)May 19, 2022
Actual (reported)June 1, 2022
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
December 1, 2023
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
January 1, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Accuracy of diagnosis of common bile duct stones in patients with low and intermediate risk by novice combined with AI-assisted and expert
the time novice finished operation and expert finished operation
Sensitivity, specificity, NPV, and PPV for the diagnosis of common bile duct stones in low and intermediate risk patients
the time novice finished operation and expert finished operation
Detection rate of gallstone lesions
the time novice finished operation and expert finished operation
Missed detection rate of gallstone lesions
the time novice finished operation and expert finished operation
Detection rate of bile duct lesions(all bile duct lesions including gallstones)
the time novice finished operation and expert finished operation
Missed rate of bile duct lesion(all bile duct lesions including gallstones)
the time novice finished operation and expert finished operation
The patient is first scanned by a novice endoscopist with the assistance of a deep learning-based bile duct scanning system during the examination, and then rescanned by a specialist without the assistance of AI.
The patient is first scanned by a specialist without the assistance of AI and then rescanned by a novice endoscopist with the assistance of a deep learning-based bile duct scanning system during the examination.
A deep learning-based bile duct scanning system that can prompt endoscopists to scan standard stations, identify bile ducts and stones in real time
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Yu Honggang, Doctor
Renmin Hospital of Wuhan University
First Submitted
May 13, 2022
First Posted
May 19, 2022
Study Start
June 1, 2022
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Primary Completion
December 1, 2023
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This estimate has passed. The registry has not confirmed an actual date for this milestone.
Study Completion
January 1, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Last Updated
May 19, 2022
Record last verified: 2022-05
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Source: ClinicalTrials.gov.
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