AI-assisted Endoscopy Report System In Improving Reporting Quality
A Single-center Study of an AI-assisted Endoscopy Report System In Improving Reporting Quality
1 other identifier
interventional
10
1 country
1
Brief Summary
In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for endoscopy report quality in endoscopists. The subjects would be divided into two groups. For the collected endoscopic videos, group A would complete the endoscopy report with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture images, prompt abnormal lesions and the parts covered by the examination (the upper gastrointestinal tract is divided into 26 parts). Group B would complete the endoscopy report without special prompts. After a period of forgetting, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the endoscopy report. Then, the completeness of the report lesion, the accuracy of the lesion location, the completeness of the lesion and the standard part in the captured images, and so on were compared with or without AI assistance.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at below P25 for not_applicable
Started Nov 2021
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
November 1, 2021
CompletedFirst Submitted
Initial submission to the registry
July 27, 2022
CompletedFirst Posted
Study publicly available on registry
July 29, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 1, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
December 1, 2022
CompletedAugust 10, 2022
July 1, 2022
1.1 years
July 27, 2022
August 8, 2022
Conditions
Outcome Measures
Primary Outcomes (4)
Integrity of report lesion
Report lesion integrity with or without AI-assisted. Calculation method = number of report lesions / total number of lesions x 100%
one month
Accuracy of lesion location
Accuracy of lesion location with or without AI-assisted. Calculation method = number of lesion with correct location / total number of lesions x 100%
one month
Integrity of lesion in captured images
Lesion integrity in captured images with or without AI-assisted. Calculation method = number of lesions in captured images / total number of lesions x 100%
one month
Integrity of standard part in captured images
Lesion integrity in captured images with or without AI-assisted. Calculation method = number of standard parts in captured images / the actual number of standard parts covered by the examination x 100%
one month
Study Arms (2)
with Artificial intelligence assistant system
EXPERIMENTALEndoscopists would complete the endoscopy report with the assistance of the artificial intelligence system.
without Artificial intelligence assistant system
NO INTERVENTIONEndoscopists would complete the endoscopy report without special prompts.
Interventions
The artificial intelligence assistant system can automatically capture images, prompt abnormal lesions and the parts covered by the examination (the stomach is divided into 26 parts).
Eligibility Criteria
You may qualify if:
- Males or females who are over 18 years old;
- After qualified medical education and obtained the Certificate of Chinese medical practitioner;
You may not qualify if:
- Doctors without qualified medical education and didn't obtaine the Certificate of Chinese medical practitioner;
- The researcher believes that the subjects are not suitable for participating in clinical trials.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Renmin Hospital of Wuhan University
Wuhan, 430060, China
Related Publications (1)
Zhang L, Lu Z, Yao L, Dong Z, Zhou W, He C, Luo R, Zhang M, Wang J, Li Y, Deng Y, Zhang C, Li X, Shang R, Xu M, Wang J, Zhao Y, Wu L, Yu H. Effect of a deep learning-based automatic upper GI endoscopic reporting system: a randomized crossover study (with video). Gastrointest Endosc. 2023 Aug;98(2):181-190.e10. doi: 10.1016/j.gie.2023.02.025. Epub 2023 Feb 25.
PMID: 36849056DERIVED
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- DEVICE FEASIBILITY
- Intervention Model
- CROSSOVER
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
July 27, 2022
First Posted
July 29, 2022
Study Start
November 1, 2021
Primary Completion
December 1, 2022
Study Completion
December 1, 2022
Last Updated
August 10, 2022
Record last verified: 2022-07
Data Sharing
- IPD Sharing
- Will not share