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Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety

NCT04719117 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 150

Last updated 2021-01-22

No results posted yet for this study

Summary

In this study, the investigators proposed a prospective study about the effectiveness of artificial intelligence system for Retrograde cholangiopancreatography. The subjects would be include in an analyses groups. The AI-assisted system helps endoscopic physicians estimate the difficulty of Endoscopic retrograde cholangiopancreatography for choledocholithiasis and make recommendations based on guidelines and difficulty scores. The investigators used the stone removal times, success rate of stone extraction and Operating time to reflect the difficulty of the operation, and evaluated whether the results of the AI system were correct.

Conditions

  • Gastrointestinal Disease
  • Endoscopy
  • Artificial Intelligence
  • Cholangiopancreatography, Endoscopic Retrograde

Sponsors & Collaborators

  • Renmin Hospital of Wuhan University

    lead OTHER

Principal Investigators

  • Honggang Yu, Doctor · Wuhan University Renmin Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-09-01
Primary Completion
2021-07-01
Completion
2021-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04719117 on ClinicalTrials.gov