Prospective Study of Capsule Endoscopy Assisted by Convolutional Neural Network in the Diagnosis of Small Bowel Diseases

Prospective Study of Capsule Endoscopy Assisted by Convolutional Neural Network in the Diagnosis of Small Intestinal Diseases

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR1900026702

Enrollment

Unknown

Registered

2019-10-19

Start date

2019-11-01

Completion date

Unknown

Last updated

2019-10-21

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

Conditions

Small Bowel Diseases

Interventions

Gold Standard:Diagnosis of capsule endoscopy images by capsule endoscopy readers with over 10 years of working experience.

Index test:Sensitivity,&#32

predictive&#32

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 1. Capsule endoscopy images are taken by patients who underwent PillCam capsule endoscopy at the endoscopy center of the researcher's hospital. 2. Patients who volunteer to participate in the study and sign the informed consent form.

Exclusion criteria

Exclusion criteria: 1. Low-quality images with many intestinal contents. 2. Low-quality images with many bubbles.

Design outcomes

Primary

Measure	Time frame
Sensitivity;Specificity;Accuracy;Positive predictive value;Negative predictive value;	—

Countries

China

Contacts

Public ContactChaohui Yu

The First Affiliated Hospital, College of Medicine, Zhejiang University

zyyyych@zju.edu.cn+86 0571 87236861

Outcome results

None listed

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