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a colorectal polyps auto-detection system based on deep learning to increase polyp detection rate: a prospective clinical study

a colorectal polyps auto-detection system based on deep learning to increase polyp detection rate: a prospective clinical study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR-DDD-17012221
Enrollment
Unknown
Registered
2017-08-02
Start date
2017-09-01
Completion date
Unknown
Last updated
2018-07-31

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

Conditions

colorectol polyp

Interventions

number&#32
per&#32
colonoscpy&#32
procedure
Gold Standard:routine colonoscpy procedure,the observation of endoscopist combined with histopathological results
Index test:polyp&#32
detection&#32
rate adenoma&#32
rate average&#32
polyp&#32

Sponsors

Sichuan Provincal People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: normal patiens for colonoscopy screening

Exclusion criteria

Exclusion criteria: FAP IBD intestinal bleeding hereditary colorectal cancer

Design outcomes

Primary

MeasureTime frame
polyp detection rate;adenoma detection rate;—

Secondary

MeasureTime frame
average polyp number per colonoscpy procedure;—

Countries

China

Contacts

Public ContactPu Wang

Sichuan Provincal People's Hospital

360649580@qq.com+86 13688060588

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 21, 2026