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Research on the identification and localization diagnosis of small bowel lesions by capsule endoscopy and CT enterography based on deep learning

Research on the identification and localization diagnosis of small bowel lesions by capsule endoscopy and CT enterography based on deep learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300077679
Enrollment
Unknown
Registered
2023-11-16
Start date
2022-07-25
Completion date
Unknown
Last updated
2023-11-21

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

Conditions

Small bowel diseases

Interventions

Observation group:None

Sponsors

The First Affiliated Hospital of Nanjing Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
14 Years to 90 Years

Inclusion criteria

Inclusion criteria: (1) Small intestine lesion identification and localization diagnosis model establishment group (n=500): Pictures of patients who underwent capsule endoscopy or CT enterography for small bowel disease at the First Affiliated Hospital of Nanjing Medical University from January 2011 to December 2020. (2) Small intestine lesion identification and localization diagnosis model test group (n=100): Pictures of patients who underwent capsule endoscopy or CT enterography for small bowel disease at the First Affiliated Hospital of Nanjing Medical University from January 2021 to December 2021. In this study, enteroscopy and surgical results were used as the localization criteria for small bowel lesions; Capsule endoscopy images included in the study used the Kingsoft OMOM capsule endoscopy system.

Exclusion criteria

Exclusion criteria: Capsule endoscopy and small intestine CT images cannot determine the localization of small bowel lesions.

Design outcomes

Primary

MeasureTime frame
AI features of pictures;

Secondary

MeasureTime frame
Segmentation of the small intestine segment;

Countries

China

Contacts

Public ContactHongjie Zhang

The First Affiliated Hospital of Nanjing Medical University

hjzhang06@163.com+86 139 5197 5918

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026