Effect of Artificial Intelligence on Polyp Miss Rate During Colonoscopy

Effect of Artificial Intelligence-Assisted Colonoscopy on Polyp Miss Prevention: A Prospective Study - L-CAD Study

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-jRCT1032260115

Enrollment

Registered

2026-06-22

Start date

2026-06-22

Completion date

Unknown

Last updated

2026-09-14

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

Conditions

Colorectal polyp

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 1. Individuals scheduled to undergo a colonoscopy or colonoscopic treatment 2. Individuals who have received a full explanation of this study and have submitted a consent form. *The purpose of the examination (screening, surveillance, diagnostic evaluation, etc.) is irrelevant.

Exclusion criteria

Exclusion criteria: 1. Patients who have undergone surgery for lower gastrointestinal tract cancer 2. Patients at high risk of bleeding for whom a pathological diagnosis via biopsy or endoscopic resection is difficult 3. Other patients whom the principal investigator deems unsuitable for participation in this study

Design outcomes

Primary

Measure	Time frame
Adenoma miss rate (AMR)	—

Secondary

Measure	Time frame
Overall polyp miss rate Miss rate of advanced colorectal neoplasia (ACN) Adenoma detection rate (ADR) Mean number of adenomas per colonoscopy (APC) Serrated lesion detection rate (SLDR)	—

Contacts

Public ContactTomoaki Matsumura

Chiba University Hospital

matsumura@chiba-u.jp+81-432262083

Outcome results