Impact of Automatic Polyp Detection System on Adenoma Detection Rate

Not Applicable

• Conditions: Colorectal Adenomas; Colonic Polyps
• Interventions: Device: Automatic polyp detection system

• Registration Number: NCT03967756
• Lead Sponsor: Changhai Hospital

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2019-05-28
• Study First Submitted QC: 2019-05-28
• Study First Posted: 2019-05-30
• Study Start: 2019-06-01
• Status Verified: 2021-04
• Last Update Submitted: 2021-04-03
• Last Update Posted: 2021-04-06
• Primary Completion: 2021-07-20
• Study Completion: 2021-10-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 1118

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

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
AI-assisted withdrawal group	Automatic polyp detection system	A deep learning-based automatic polyp detection system was used to assist the endoscopist.

Primary Outcome Measures

Name	Time	Method
adenoma detection rate(ADR)	30 minutes	the number of patients with at least one adenoma divided by the total number of patients.

Secondary Outcome Measures

Name	Time	Method
adenoma per colonoscopy	30 minutes	the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
polyp per colonoscopy	30 minutes	the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.
polyp detection rate(PDR)	30 minutes	the number of patients with at least one polyp divided by the total number of patients.

Trial Locations

Locations (1)

Changhai Hospital, Second Military Medical University; 🇨🇳 Shanghai, China