Deep learning model using 3D convolutional neural network to predict the outcome of biliary cannulation

Deep learning model using 3D convolutional neural network to predict the outcome of biliary cannulation - Deep learning model using 3D convolutional neural network to predict the outcome of biliary cannulation

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000060342

Enrollment

800

Registered

2026-02-01

Start date

2025-05-20

Completion date

Unknown

Last updated

2026-06-29

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

Conditions

Hepato-biliopancreatic dieases

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients with native papilla who underwent biliary ERCP recorded on digital video from April 2017 to December 2024.

Exclusion criteria

Exclusion criteria: Patients with surgically altered anatomy, such as Billroth II or Roux-en-Y reconstruction

Design outcomes

Primary

Measure	Time frame
Diagnostic accuracy of deep learning model to predict the outcome of biliary cannulation (conventional or rescue methods)	—

Countries

Japan

Contacts

Public ContactHiroo Imazu

Teikyo University School of Medicine Department of Internal Medicine

imazu.hiroo.cx@teikyo-u.ac.jp03-3964-1211

Outcome results