Quality Improvement Intervention in Colonoscopy Using Artificial Intelligence
NCT ID: NCT03622281
Last Updated: 2020-02-12
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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COMPLETED
NA
676 participants
INTERVENTIONAL
2018-10-20
2019-05-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
DOUBLE
Study Groups
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Colonoscopists who received quality intervention
quality improvement intervention using artificial intelligence
Colonoscopists received performance measure monitoring and feedback
Colonoscopists who did not received quality intervention
No interventions assigned to this group
Interventions
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quality improvement intervention using artificial intelligence
Colonoscopists received performance measure monitoring and feedback
Eligibility Criteria
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Inclusion Criteria
* agree to give written informed consent.
Exclusion Criteria
* patients with a history of inflammatory bowel disease (IBD), CRC, colorectal surgery;
* patients with prior failed colonoscopy and high suspicion of polyposis syndromes, IBD and typical advanced CRC;
* patients refused to participate in the trial;
* the colonoscopyprocedure cannot be completed due to stenosis, obstruction, huge occupying lesions, or solid stool;
* the colonoscopy procedure have to be terminated due to complications of anaesthesia.
18 Years
80 Years
ALL
Yes
Sponsors
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Shandong University
OTHER
Responsible Party
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Yanqing Li
Vice president of QiLu Hospital
Locations
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Department of Gastroenterology, Qilu Hospital, Shandong University
Jinan, Shandong, China
Countries
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References
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Su JR, Li Z, Shao XJ, Ji CR, Ji R, Zhou RC, Li GC, Liu GQ, He YS, Zuo XL, Li YQ. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc. 2020 Feb;91(2):415-424.e4. doi: 10.1016/j.gie.2019.08.026. Epub 2019 Aug 24.
Other Identifiers
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2018SDU-QILU-716
Identifier Type: -
Identifier Source: org_study_id
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