A Convolutional Neural Network for Difficult Biliary Cannulation

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Conditions studied: Difficult Biliary Cannulation

In brief

The main purpose of the study is to train a convolutional neural network (CNN) to predict difficult biliary canulation (DBC) following the European Society of Gastrointestinal Endoscopy Society (ESGE). Consecutive patients undergoing an endoscopic retrograde cholangiopancreatography (ERCP) will be included in the study. Several pictures of the second portion of the duodenum including the ampulla will be taken, along with several pictures of the radiological image. Pictures prospectively collected from the study PRECABIDO (NCT06591364), a multicenter study whith the purpose of evaluating the prevalence of difficult biliary cannulation and predictive factors for difficult cannulation and cannulation failure using ESGE criteria were also used for the training of the CNN. We will also assess: A validation of the CNN assessing the agreement between ESGE criteria and the CNN prediction. To design a novel application based on the use of a convolutional neural network (CNN) to detect difficult biliary cannulation. .

Key facts

Study ID

NCT07389915

Run by

University of La Laguna

People needed

600

Starts

2026-02-01

Expected to finish

2027-03-31

Last updated by the study team

2026-02-05

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Age >18 years
• Signed informed consent
• Patients indicated for ERCP

You may not qualify if…

• INR > 1.5
• Platelets < 50,000/mm³
• Patients with a prior endoscopic sphincterotomy
• Papilla of Vater not accessible via duodenoscope (gastric or duodenal stenosis due to neoplasm) or gastric surgery (Billroth II, Roux-en-Y)
• Known pancreas divisum
• Indication due to pancreatic duct pathology

Full record on ClinicalTrials.gov

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