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A Study of the Usefulness of an AI (Artificial Intelligence) Automated Endoscopic Diagnosis System in Colonoscopy - A Prospective Non-Randomized Controlled Trial (Non-Inferiority Study)

A Study of the Usefulness of an AI (Artificial Intelligence) Automated Endoscopic Diagnosis System in Colonoscopy - A Prospective Non-Randomized Controlled Trial (Non-Inferiority Study) - A prospective study of CADe

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
JPRN
Registry ID
JPRN-UMIN000044890
Enrollment
120
Registered
2021-08-01
Start date
2021-08-01
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 lesion

Interventions

None listed

Sponsors

Division of Gastroenterology and Hepatology, St. Marianna University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: (1) Patients with polyps who have undergone colonoscopy by a expert in the previous year and polyps are not treated and are under observation (2) Age: 20 to 90 years old (age at the time of registration) (3) PS: ECOG performance status score: 0 to 2 (4) Patients who have not been previously treated for colorectal lesions, including endoscopic treatment. (5) Patients who can follow Guidelines for gastroenterological endoscopy in patients undergoing antithrombotic treatment (6) Patients who have given written consent to participate in the study.

Exclusion criteria

Exclusion criteria: (1) Patients scheduled for endoscopic treatment of the target lesion (2) Patients who are judged by the physician in charge to have a bleeding tendency (3) Patients who deviate from or are unable to comply with the "Guidelines for Gastrointestinal Endoscopy for Patients Taking Antithrombotic Drugs". (4) Patients with inflammatory bowel disease or familial adenomatous polyposis. (5) Patients who are judged inappropriate by the investigator.

Design outcomes

Primary

MeasureTime frame
Polyp detection rate of non-experts with AI-assisted computer-aided detection system is non-inferior to that of experts.

Secondary

MeasureTime frame
1. Mean number of adenomas per procedure 2. Polyp detection rate 3. Total procedure time 4. Insertion time 5. Withdrawal time

Countries

Japan

Contacts

Public ContactTadateru Maehata

St. Marianna University School of Medicine Division of Gastroenterology and Hepatology

t2maehata@marianna-u.ac.jp0449778111

Outcome results

None listed

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