Explore the Relationship Between the Percentage of Colonoscopy Withdrawal Overspeed and the ADR

NCT ID: NCT05444166

Last Updated: 2023-03-29

Study Results

Results pending

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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Recruitment Status

UNKNOWN

Total Enrollment

550 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-07-29

Study Completion Date

2023-11-29

Brief Summary

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In this study, the investigators used the optical flow method to measure the colonoscopy withdrawal speed, and doctors were selected from multiple hospitals to collect prospective colonoscopy screening videos, and the percentage of colonoscopy withdrawal overspeed was calculated to explore the relationship between it based on optical flow method and the adenoma detection rate.

Detailed Description

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Study showed that adequate withdrawal time is an important prerequisite for full mucosal inspection. In a large population-based analysis, a 1-min increase in withdrawal time led to a 3.6% increase in the ADR. Protocols or expert consensus recommend a standard withdrawal time of 6 min or longer. However, Findings of studies showed that a number of colonoscopies had a withdrawal time less than 6 min, which greatly reduces the ADR.

Investigator's preliminary experiments have shown that deep learning can monitor the colonoscopy withdrawal time in real-time and improve the adenoma detection rate. Based on the above rich foundation of preliminary work and the massive demand for improving the colonoscopy withdrawal assessment system.

The investigators improved EndoAngel to use optical flow method to monitor the colonoscopy withdrawal speed. The performance of the EndoAngel system was verified in colonoscopy videos. The investigators then aimed to evaluate whether the EndoAngel system could improve polyp detection rate after restricting the colonoscopy withdrawal speed.

Conditions

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Colonoscopy Artificial Intelligence Gastrointestinal Disease Deep Learning

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

1. Male or female ≥18 years old;
2. Able to read, understand and sign an 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 screening colonoscopy.

Exclusion Criteria

1. Have drug or alcohol abuse or mental disorder in the last 5 years;
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 a 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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Renmin Hospital of Wuhan University

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Renmin Hospital

Hubei, Wuhan, Hubei, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Honggang Yu, Doctor

Role: CONTACT

8613871281899

Chaijie Luo, Doctor

Role: CONTACT

8619828616210

Facility Contacts

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Honggang Yu, Doctor

Role: primary

8613871281899

Chaijie Luo, Doctor

Role: backup

8619828616210

Other Identifiers

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EA-22-020

Identifier Type: -

Identifier Source: org_study_id

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