Effect of AI Monitoring Blind Spots of EGD on the Inspection Time and Lesion Dection Rate
NCT ID: NCT05832411
Last Updated: 2023-06-22
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
1672 participants
INTERVENTIONAL
2023-07-01
2024-08-31
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
OTHER
SINGLE
Study Groups
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AI group
In the AI group, except for the original videos, there is additional information presented to endoscopists:（1）the virtual stomach model monitoring;（2）time;（3）scoring. Endoscopists will complete EGD examination without blind spots.
Endoangle
there is additional information presented to endoscopists:（1）the virtual stomach model monitoring；（2）time;（3）scoring.
Routine group
In the Routine group, only the original videos and there is no additional information, and inspection time will be no less than 7 minutes.
No interventions assigned to this group
Interventions
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Endoangle
there is additional information presented to endoscopists:（1）the virtual stomach model monitoring；（2）time;（3）scoring.
Eligibility Criteria
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Inclusion Criteria
2. patients able to give informed consent
3. American Society of Anesthesiology risk class 1,2 or 3
Exclusion Criteria
2. patients with a history of previous gastrectomy
3. patients with a serious underlying disease
4. pregnant patients
5. researchers believe that the patient is not suitable to participate in the trial
18 Years
ALL
Yes
Sponsors
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Renmin Hospital of Wuhan University
OTHER
Responsible Party
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References
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Wu L, Zhang J, Zhou W, An P, Shen L, Liu J, Jiang X, Huang X, Mu G, Wan X, Lv X, Gao J, Cui N, Hu S, Chen Y, Hu X, Li J, Chen D, Gong D, He X, Ding Q, Zhu X, Li S, Wei X, Li X, Wang X, Zhou J, Zhang M, Yu HG. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut. 2019 Dec;68(12):2161-2169. doi: 10.1136/gutjnl-2018-317366. Epub 2019 Mar 11.
Wu L, Xu M, Jiang X, He X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Guo M, Huang M, Ye L, Shen L, Yu H. Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc. 2022 Feb;95(2):269-280.e6. doi: 10.1016/j.gie.2021.09.017. Epub 2021 Sep 20.
Bisschops R, Areia M, Coron E, Dobru D, Kaskas B, Kuvaev R, Pech O, Ragunath K, Weusten B, Familiari P, Domagk D, Valori R, Kaminski MF, Spada C, Bretthauer M, Bennett C, Senore C, Dinis-Ribeiro M, Rutter MD. Performance measures for upper gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy. 2016 Sep;48(9):843-64. doi: 10.1055/s-0042-113128. Epub 2016 Aug 22. No abstract available.
Tan X, Yao L, Dong Z, Li Y, Yu Y, Gao X, Zhu K, Su W, Yin H, Wang W, Luo C, Li J, You H, Hu H, Zhou W, Yu H. Artificial Intelligence as a Surrogate for Inspection Time to Assess Completeness in Esophagogastroduodenoscopy: A Prospective, Randomized, Noninferiority Study. Clin Transl Gastroenterol. 2025 Mar 25;16(6):e00839. doi: 10.14309/ctg.0000000000000839. eCollection 2025 Jun 1.
Other Identifiers
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EA-23-005
Identifier Type: -
Identifier Source: org_study_id
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