Impact of Automatic Polyp Detection System on Adenoma Detection Rate

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

Clinical Phase

NA

Total Enrollment

1118 participants

Study Classification

INTERVENTIONAL

Study Start Date

2019-06-01

Study Completion Date

2021-10-01

Brief Summary

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In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. The primary objective of this study is to examine whether a combination of colonoscopy and a deep learning-based automatic polyp detection system is a feasible way to increase adenoma detection rate compared to standard colonoscopy.

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Detailed Description

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Conditions

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Colonic Polyps Colorectal Adenomas

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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AI-assisted withdrawal group

A deep learning-based automatic polyp detection system was used to assist the endoscopist.

Group Type EXPERIMENTAL

Automatic polyp detection system

Intervention Type DEVICE

When colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system， there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm.

Routine withdrawal group

Routine withdrawal without any assist.

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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Automatic polyp detection system

When colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system， there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic.
* Patients who have signed inform consent form.

Exclusion Criteria

* Patients who have undergone colonic resection
* Patients with intracranial and/or central nervous system disease, including cerebral infarction and cerebral hemorrhage.
* Patients with severe chronic cardiopulmonary and renal disease.
* Patients who are unwilling or unable to consent.
* Patients who are not suitable for colonoscopy
* Patients who received urgent or therapeutic colonoscopy
* Patients with pregnancy, inflammatory bowel disease, polyposis of colon, colorectal cancer, or intestinal obstruction
* Patients who are taking aspirin, clopidogrel or other anticoagulants
* Patients with withdrawal time \< 6 min

Minimum Eligible Age

40 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No