Construction of an Artificial Intelligence–Based Comprehensive Quality Control Scoring Model for Colonoscopy and Evaluation of Its Predictive Performance for Adenoma Detection Rate

Status

Recruiting

Phases

Phase 4

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2600129159

Enrollment

Unknown

Registered

2026-07-31

Start date

2026-08-01

Completion date

Unknown

Last updated

2026-08-03

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

Conditions

colorectal adenoma

Interventions

Validation cohort:None

Derivation cohort:None

Eligibility

Sex/Gender

All

Age

18 Years to 85 Years

Inclusion criteria

Inclusion criteria: 1. Age 18–85 years; 2. Undergoing complete colonoscopy with documented intubation of the cecum.

Exclusion criteria

Exclusion criteria: 1. History of prior colorectal resection; 2. Inflammatory bowel disease, familial polyposis syndromes, other hereditary polyposis syndromes, non-hereditary polyposis syndromes (e.g., Cronkhite–Canada syndrome), or advanced colorectal cancer causing luminal stenosis that prevented completion of total colonoscopy; 3. Incomplete recording of AI quality control data.

Design outcomes

Primary

Measure	Time frame
adenoma detection rate;	—

Secondary

Measure	Time frame
advanced adenoma detection rate;adenomas per colonoscopy;polyp detection rate,;polyps per colonoscopy;Sessile Serrated Lesion Detection Rate;	—

Countries

China

Contacts

Public ContactXiaobo Li

Renji Hospital affiliated to Shanghai Jiaotong University School of Medicine

lxb_1969@163.com+86 21 68383015

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Aug 10, 2026