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Machine learning based on the gallbladder morphology for screening biliary atresia among infants with conjugated hyperbilirubinemia

Machine learning based on the gallbladder morphology for screening biliary atresia among infants with conjugated hyperbilirubinemia

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR1800017428
Enrollment
Unknown
Registered
2018-07-30
Start date
2018-08-15
Completion date
Unknown
Last updated
2018-08-20

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

Conditions

Biliary atresia

Interventions

Gold Standard:Surgical exploration, puncture biopsy, intraoperative cholangiography or percutaneous cholecystography
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Sponsors

Department of Medical Ultrasonics, the First Affiliated Hospital of Sun Yat-sen University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Age34.2umol/L meanwhile direct bilirubin level>17.1umol/L; 3. A gallbladder could be detected on a high frequent ultrasound probe (>7MHz).

Exclusion criteria

Exclusion criteria: 1. Jaundice caused by abdominal mass; 2. Lost to follow up and could not get final diagnosis.

Design outcomes

Primary

MeasureTime frame
gallbladder morphology;sensitivity;specificity;positive predictive value;negative predictive value;accuracy;AUROCs;—

Secondary

MeasureTime frame
Fasting time;—

Countries

China

Contacts

Public ContactZhou Luyao

The First Affiliated Hospital of Sun Yat-Sen University

zhouly6@mail.sysu.edu.cn+86 13427539467

Outcome results

None listed

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