A Study on the Effectiveness of AI-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
NCT ID: NCT04912037
Last Updated: 2021-06-03
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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UNKNOWN
NA
385 participants
INTERVENTIONAL
2021-06-01
2022-02-01
Brief Summary
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Detailed Description
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In recent years, deep learning algorithms have been continuously developed and increasingly mature.They have been gradually applied to the medical field. Computer vision is a science that studies how to make machines "see". Through deep learning, camera and computer can replace human eyes to carry out machine vision such as target recognition, tracking and measurement.Interdisciplinary cooperation in the field of medical imaging and computer vision is also one of the research hotspots in recent years. At present, it is mainly applied to the automatic identification and detection of lesions and quality control, and has achieved good results.
Our preliminary experiments have shown that deep learning has a high accuracy in endoscopic quality monitoring, which can effectively regulate doctors' operations, reduce blind spots and improve the quality of endoscopic examination.At the same time, it can also monitor the doctor's withdrawal time in real time and improve the detection rate of adenoma.In the previous work of our research group, we have successfully developed deep learning-based colonoscopy withdraw speed monitoring and intestinal cleanliness assessment, and verified the effectiveness of the AI-assisted system（EndoAngel） in improving the quality of gastroscopy and colonoscopy in clinical trials.
Based on the above rich foundation of preliminary work, as well as the huge demand in the field of colonoscopy training,By comparing the colonoscopy operation training for novices with and without EndoAngel assistance, we plan to compare the colonoscopy learning effect of novices with and without assistance, including skill results and cognitive level, to explore whether AI can promote the improvement of the colonoscopy operation training for novices.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
DIAGNOSTIC
DOUBLE
Study Groups
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with AI-assisted system
The novice doctors are trained in colonoscopy with an artificial intelligence assisted system that can indicate abnormal lesions and the speed of withdrawal in real time, as well as feedback on the percentage of overspeed.
artificial intelligence assistance system
the artificial intelligence assistance system can indicate abnormal lesions and real-time withdrawal speed, and feedback the overspeed percentage.
without AI-assisted system
The novice doctors receive routine colonoscopy training without artificial intelligence assistance system and no special tips
No interventions assigned to this group
Interventions
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artificial intelligence assistance system
the artificial intelligence assistance system can indicate abnormal lesions and real-time withdrawal speed, and feedback the overspeed percentage.
Eligibility Criteria
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Inclusion Criteria
2. Able to read, understand and sign informed consent
3. The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures
4. Patients requiring colonoscopy
Exclusion Criteria
2. Pregnant or lactating women
3. Patients with known multiple polyp syndrome;
4. patients with known inflammatory bowel disease;
5. known intestinal stenosis or space-occupying tumor;
6. known colon obstruction or perforation;
7. patients with a history of colorectal surgery;
8. Patients with previous history of allergy to pre-used spasmolysis;
9. Unable to perform biopsy and polyp removal due to coagulation disorders or oral anticoagulants;
10. High risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.
50 Years
ALL
No
Sponsors
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Renmin Hospital of Wuhan University
OTHER
Responsible Party
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Principal Investigators
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Yu w Honggang, Doctor
Role: PRINCIPAL_INVESTIGATOR
Renmin Hospital of Wuhan University
Locations
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Renmin hospital of Wuhan University
Wuhan, Hubei, China
Countries
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Central Contacts
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Facility Contacts
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Provided Documents
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Document Type: Study Protocol, Statistical Analysis Plan, and Informed Consent Form
Other Identifiers
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EA-21-005
Identifier Type: -
Identifier Source: org_study_id
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