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Artificial Intelligence-assisted Colonoscopy for Detection of Colon Polyps

Artificial Intelligence-assisted Colonoscopy for Detection of Colon Polyps: a Prospective Randomized Cohort Study

Status
UNKNOWN
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT04126265
Enrollment
560
Registered
2019-10-15
Start date
2019-09-01
Completion date
2020-08-31
Last updated
2020-01-02

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

Conditions

Artificial Intelligence, Colonoscopy

Brief summary

All subjects shall sign informed consent before screening, and subjects shall be included according to inclusion and exclusion criteria. A total of four endoscopists were included in the study, two in each group of senior endoscopists and two in each group of junior endoscopists. Patients were randomly enrolled into the senior endoscopy group and the junior endoscopy group, and received artificial intelligence assisted colonoscopy and conventional colonoscopy successively. The two colonoscopy methods were performed back to back by different endoscopy physicians with the same seniority. All patients were examined and treated according to routine medical procedures. The routine colonoscopy group and the artificial-intelligence-assisted colonoscopy group made detailed records of the patients' withdrawal time, entry time, number of polyps detected, polyp Paris classification, polyp size, polyp shape, polyp location and intestinal preparation during the colonoscopy process

Detailed description

This is a prospective randomized clinical study.This study was conducted in the Endoscopy Center of the Nanfang Hospital, China. Routine bowel preparation consisted of 4 L of polyethylene glycol, given in split doses. Colonoscopies were performed with high definition colonoscopes and high-definition monitors. All subjects shall sign informed consent before screening, and subjects shall be included according to inclusion and exclusion criteria. A total of four endoscopists were included in the study, two in each group of senior endoscopists (\>1000 colonoscopies) and two in each group of junior endoscopists ( \<1000 colonoscopies). Patients were randomly enrolled into the senior endoscopy group and the junior endoscopy group, and received artificial intelligence assisted colonoscopy and conventional colonoscopy successively. The two colonoscopy methods were performed by different endoscopy physicians back to back with the same seniority. All patients were examined and treated according to routine medical procedures (outpatient patients and inpatients who did not sign the consent form for polypectomy were not resected for the lesions detected during the examination, while inpatients who signed the consent form for polypectomy were left in the original position after the first colonoscopy and removed at the end of the second examination). The routine colonoscopy group and the artificial-intelligence-assisted colonoscopy group made detailed records of the patients' withdrawal time, entry time, number of polyps detected, polyp Paris classification, polyp size, polyp shape, polyp location and intestinal preparation during the colonoscopy process.

Interventions

DEVICEArtificial intelligence assisted colonoscopy

The colonoscopy is connected to the real-time polyp detection system. If the polyp is detected by enteroscopy, the alarm will be given.

Sponsors

Side Liu
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes

Inclusion criteria

Chinese population aged 18-80 years old; Patients voluntarily signed informed consent form; In accordance with the indications of colonoscopy.

Exclusion criteria

(IBD) history of inflammatory bowel disease; History of colorectal surgery; Previous failed colonoscopy; Polyposis syndrome; Highly suspected colorectal cancer (CRC)

Design outcomes

Primary

MeasureTime frameDescription
Detection rate of small polyps (diameter < 6mm)6 monthsIn each group, the number of patients with small polyps was detected as a percentage of the total number of patients.

Secondary

MeasureTime frameDescription
Number of polyps detected6 monthsNumber of polyps found in each group.
Polyp size6 monthsAverage size of all polyps detected in each group.
Polyp morphology6 monthsMorphological classification of all polyps detected in each group.

Countries

China

Contacts

Primary ContactYI zhang, master degree
13533787871@163.com+86 13533787871

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026