Artificial Intelligence in Colonoscopy for Cancer Prevention -a Randomized Health Service Implementation Project-

Artificial Intelligence in Colonoscopy for Cancer Prevention -a Randomized Health Service Implementation Project- - ACCEPT project

Status

Recruiting

Phases

Unknown

Study type

Interventional

Source

JPRN

Registry ID

JPRN-UMIN000044748

Enrollment

220000

Registered

2021-07-03

Start date

2021-07-03

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Colorectal neoplasm

Interventions

Real-time use of AI for polyp detection during colonoscopy Colonoscopy without use of AI

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Eligible for inclusion are participants in the national cancer screening programmes in Norway, Poland, Spain, Italy, Denmark, and Japan who undergo either primary colonoscopy screening or colonoscopy after a positive faecal immunochemical screening test.

Exclusion criteria

Exclusion criteria: Exclusion criteria for the screening programmes are applied in the present study (e.g., individuals with previous history of colorectal cancer).

Design outcomes

Primary

Measure	Time frame
-Short-term primary endpoints 1. Adenoma detection rate (ADR) 2. Advanced adenoma detection rate (A-ADR) -Long-term primary endpoints 1. Incidence of colorectal cancers after 10-years follow-up 2. Mortality from colorectal cancers after 10-years follow-up	—

Secondary

Measure	Time frame
1. Proportion of endoscopists with ADRs 25% or higher 8 2. Serrated polyp detection rate 3. Mean number of adenomas per colonoscopy 4. Learning effect of using AI during colonoscopy 5. Benefits from the use of AI according to the expertise 6. Comparison of different AI systems 7. Colonoscopy performance (e.g., cecal intubation rate, insertion and withdrawal time). 8. Rate of adverse events during and within 30 days after colonoscopy 9. Incidence of colorectal cancers in 15-year follow-up 10. Mortality from colorectal cancers in 15-year follow-up 11. All-cause mortality in 10-year and 15-year follow-up 12. Mortality due to causes other than colorectal cancer in 10-year and 15-year follow-up 13. Cost-effectiveness of using AI in a cancer screening programme for colorectal cancer. 14. Retrospective analysis of recorded videos for individuals who have cancer or relevant lesions detected after colonoscopy screening.	—

Measure

Time frame

1. Proportion of endoscopists with ADRs 25% or higher 8 2. Serrated polyp detection rate 3. Mean number of adenomas per colonoscopy 4. Learning effect of using AI during colonoscopy 5. Benefits from the use of AI according to the expertise 6. Comparison of different AI systems 7. Colonoscopy performance (e.g., cecal intubation rate, insertion and withdrawal time). 8. Rate of adverse events during and within 30 days after colonoscopy 9. Incidence of colorectal cancers in 15-year follow-up 10. Mortality from colorectal cancers in 15-year follow-up 11. All-cause mortality in 10-year and 15-year follow-up 12. Mortality due to causes other than colorectal cancer in 10-year and 15-year follow-up 13. Cost-effectiveness of using AI in a cancer screening programme for colorectal cancer. 14. Retrospective analysis of recorded videos for individuals who have cancer or relevant lesions detected after colonoscopy screening.

—

Countries

Japan,Europe

Contacts

Public ContactYuichi Mori

University of Oslo Clinical Effectiveness Research Group

ibusiginjp@gmail.com0047-40894135

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026