Effect of Two Colonoscopy AI Systems for Colon Polyp Detection

Colonoscopy Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05089071
• Other study ID #: SNUH IRB 2107-235-1240
• Status: Completed
• Phase: N/A
• Start date: November 1, 2021
• Est. completion date: December 31, 2022

Study information

• Verified date: March 2022
• Source: Seoul National University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 3046
• Est. completion date: December 31, 2022
• Est. primary completion date: October 31, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 45 Years to 100 Years

Eligibility

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

Related Conditions & MeSH terms

• Adenoma
• Colonoscopy
• Sessile Serrated Adenoma

Intervention

Device:
• Assist by artificial intelligence system for colon polyp detection
Assist by artificial intelligence system for colon polyp detection

Locations

Country	Name	City	State
Korea, Republic of	Healthcare System Gangnam Center, Seoul National University Hospital	Seoul

Sponsors (2)

Lead Sponsor	Collaborator
Seoul National University Hospital	Seoul National University

Country where clinical trial is conducted

Korea, Republic of, 

References & Publications (1)

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Adenoma detection rate	proportion of colonoscopies with at least one adenoma detected overall and as detected by the physician.	12 months
Primary	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	polyp detection rate	proportion of colonoscopies with at least one polyp detected overall and as detected by the physician.	12 months