AI-assisted Detection of Missed Colonic Polyps

NCT ID: NCT04227795

Last Updated: 2020-03-04

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 52 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-01-01
• Study Completion Date: 2020-03-01

Brief Summary

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

Detailed Description

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

The primary endoscopist conducted the colonoscopic examination in the usual manner. All colonoscopy procedures were performed with high-definition colonoscopes (EVIS-EXERA 290 video system, Olympus Optical, Tokyo, Japan). The colonoscopy was first advanced to the cecum in all patients as confirmed by identification of the appendiceal orifice and ileocecal valve or by intubation of the ileum. After cecal intubation, the colonoscopy was slowly withdrawn to the rectum by the primary endoscopist. The AI real time detection was then activated with the output displayed in a different monitor and was only viewed by an independent investigator, who was an experienced endoscopist. The primary endoscopist was blinded to the AI real time detection result al.

The colon was divided into three segments during the examination: right side, transverse and left side colon, using hepatic flexure and splenic flexure as dividing landmark. All polyps were marked for size (measured with biopsy forceps), location and morphology according to the Paris classification, and then removed or biopsied for histological examination. After examination of each segment, segmental unblinding of the AI results were provided by the independent viewer. If additional polyps were detected by AI but not by the endoscopist, that segment were reexamined to look for the missed polyp. If no additional polyp was detected by the AI, the next colonic segment was examined. Missed lesions were defined as lesions identified by AI and then confirmed on reexamination by the endoscopist.

The first withdrawal time (minus the polypectomy site) was measured. The Boston Bowel Preparation Scale score (BPPS) was used for evaluation of bowel cleanliness.

Conditions

Colon Adenoma; Colonic Polyp; Colon Cancer

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Artificial intelligence-Assisted real time colonoscopy

AI assisted real-time detection of colonic lesions

Group Type: EXPERIMENTAL

Artificial intelligence-Assisted real time colonoscopy

Intervention Type: DEVICE

The colonoscopy was performed under artificial intelligence assistance

Interventions

Artificial intelligence-Assisted real time colonoscopy

The colonoscopy was performed under artificial intelligence assistance

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

• 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.

• Minimum Eligible Age: 40 Years
• Maximum Eligible Age: 90 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

The University of Hong Kong

OTHER

Sponsor Role: lead

Responsible Party

LEUNG Wai Keung

Clinical Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Ka Luen, Thomas Lui

Role: PRINCIPAL_INVESTIGATOR

Queen Mary Hospital, the University of Hong Kong

Locations

Queen Mary Hospital

Hong Kong, , Hong Kong

Countries

Hong Kong

References

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.

Reference Type: DERIVED

PMID: 32376335 (View on PubMed)

Other Identifiers

UW 19-309

Identifier Type: -

Identifier Source: org_study_id