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Machine Learning Based on Sonographic Gallbladder Images in Prenatal Diagnosis of Biliary Atresia

Machine Learning Based on Sonographic Gallbladder Images in Prenatal Diagnosis of Biliary Atresia

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200059705
Enrollment
Unknown
Registered
2022-05-08
Start date
2022-05-01
Completion date
Unknown
Last updated
2024-03-25

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
Index test:Intelligent prenatally diagnostic system based on sonographic gallbladder images

Sponsors

Shengjing Hospital of China Medical University
Lead Sponsor

Eligibility

Sex/Gender
Female

Inclusion criteria

Inclusion criteria: 1. 18-38 weeks of pregnancy (examination over 4 weeks is counted as new pregnancy); 2. The fetal gallbladder is visible during the examination.

Exclusion criteria

Exclusion criteria: Lost to follow-up, unable to obtain a definite diagnosis.

Design outcomes

Primary

MeasureTime frame
sensitivity;specificity;positive predictive value;negative predictive value;accuracy;area under the ROC curve;—

Countries

China

Contacts

Public ContactLizhu Chen

Shengjing Hospital of China Medical University

aliceclz@sina.com+86 18940256778

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 5, 2026