AI-assisted Detection of Missed Colonic Polyps

NCT04227795 | Completed

• 1 other identifier: UW 19-309
• Study Type: interventional
• Target: 52
• Locations: 1 country
• Sites: 1

Brief Summary

A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps

Conditions

Colon Adenoma; Colonic Polyp; Colon Cancer

Keywords

Artificial intelligence; Colonoscopy; Deep learning; Detection; Computer-aided

Outcome Measures

Primary Outcomes (1)

• Adenoma miss rate

  The number of patient had at least one missed adenoma

  During the colonoscopy procedure

Secondary Outcomes (3)

• Total number of adenoma missed

  During the colonoscopy procedure

• Colonic polyp miss rate

  During the colonoscopy procedure

• Total number of missed polyps

  During the colonoscopy procedure

Study Arms (1)

Artificial intelligence-Assisted real time colonoscopy

EXPERIMENTAL

AI assisted real-time detection of colonic lesions

Device: Artificial intelligence-Assisted real time colonoscopy

Interventions

Artificial intelligence-Assisted real time colonoscopy DEVICE

The colonoscopy was performed under artificial intelligence assistance

Artificial intelligence-Assisted real time colonoscopy

Eligibility Criteria

• Age: 40 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate

You may not qualify if:

• Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses.
• Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.

Sponsors & Collaborators

• The University of Hong Kong: lead

Study Sites (1)

Queen Mary Hospital

Hong Kong, Hong Kong

Location

Related Publications (1)

• Lui TKL, Hui CKY, Tsui VWM, Cheung KS, Ko MKL, Foo DCC, Mak LY, Yeung CK, Lui TH, Wong SY, Leung WK. New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video). Gastrointest Endosc. 2021 Jan;93(1):193-200.e1. doi: 10.1016/j.gie.2020.04.066. Epub 2020 May 4.

  PMID: 32376335 DERIVED

MeSH Terms

Conditions

Colonic Polyps; Colonic Neoplasms

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases

Study Officials

• Ka Luen, Thomas Lui

  Queen Mary Hospital, the University of Hong Kong

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: January 10, 2020
• First Posted: January 14, 2020
• Study Start: January 1, 2020
• Primary Completion: February 1, 2020
• Study Completion: March 1, 2020
• Last Updated: March 4, 2020

Record last verified: 2020-03

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)