Effect of Two Colonoscopy AI Systems for Colon Polyp Detection

Effect of Two Colonoscopy AI Systems for Colon Polyp Detection According to the False Positive Rates of the Systems: A Single-center Prospective Study

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. The investigators aim to identify the effect of two CADe systems according to the system performance on false positive rate

Study Overview

• Status: Completed
• Conditions: Adenoma; Colonoscopy; Sessile Serrated Adenoma
• Intervention / Treatment: Device: Assist by artificial intelligence system for colon polyp detection

Detailed Description

Artificial intelligence technology based on deep learning is being applied in various medical fields, and research is being actively conducted to develop computer-aided detection (CADe) systems for colonoscopies to overcome the limitation of the variance of human skills. These well-trained CADe systems demonstrated high performance for neoplastic polyp detection and reported a 44% increase in adenoma detection rate (ADR) for endoscopists. However, the level of performance in the CADe system is not clear for expert endoscopists to be useful for ADR increase.

Furthermore, false positives(FPs) of the CADe system may negatively influence ADR during a screening colonoscopy. Accordingly, the investigators sought to identify the effect of the colonoscopy CADe system according to FP performance in endoscopists with various levels. The investigators hypothesized that the CADe system with low FPs would be useful to prevent the decrease in ADR in case of a high endoscopy workload according to the performance of CADe systems.

• Study Type: Interventional
• Enrollment (Actual): 3046
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Korea, Republic of
  • Seoul, Korea, Republic of
    • Healthcare System Gangnam Center, Seoul National University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 100 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

patient for screening or surveillance colonoscopy patients agreed with participating in the study

Exclusion Criteria:

patients who do not agree with participating in the study patients with a history of colon resection patients with a history of inflammatory bowel resection patients with poor bowel preparation

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: CADe group; Endoscopists perform colonoscopy with CADe system	Device: Assist by artificial intelligence system for colon polyp detection; Assist by artificial intelligence system for colon polyp detection
No Intervention: Control; Endoscopists perform colonoscopy without CADe system

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Adenoma detection rate	proportion of colonoscopies with at least one adenoma detected overall and as detected by the physician.	12 months
Sessile serrated lesion detection rate	proportion of colonoscopies with at least one sessile serrated lesion detected overall and as detected by the physician.	12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
polyp detection rate	proportion of colonoscopies with at least one polyp detected overall and as detected by the physician.	12 months

Collaborators and Investigators

• Sponsor: Seoul National University Hospital
• Collaborators: Seoul National University
• Investigators: Principal Investigator: Jung Ho Bae, MD, Seoul National University Hospital

Publications and helpful links

General Publications

• Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, Antonelli G, Yu H, Areia M, Dinis-Ribeiro M, Bhandari P, Sharma P, Rex DK, Rosch T, Wallace M, Repici A. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021 Jan;93(1):77-85.e6. doi: 10.1016/j.gie.2020.06.059. Epub 2020 Jun 26.

Study record dates

Study Major Dates

• Study Start (Actual): November 1, 2021
• Primary Completion (Actual): October 31, 2022
• Study Completion (Actual): December 31, 2022

Study Registration Dates

• First Submitted: October 11, 2021
• First Submitted That Met QC Criteria: October 11, 2021
• First Posted (Actual): October 22, 2021

Study Record Updates

• Last Update Posted (Actual): July 27, 2023
• Last Update Submitted That Met QC Criteria: July 25, 2023
• Last Verified: March 1, 2022

More Information

Terms related to this study

• Keywords: colonoscopy; endoscopist; adenoma detection rate; artificial intelligence
• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Adenoma
• Other Study ID Numbers: SNUH IRB 2107-235-1240

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No