A Convolutional Neural Network for Difficult Biliary Cannulation
NCT07389915 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 600
Last updated 2026-02-05
Summary
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.
.
Conditions
- Difficult Biliary Cannulation
Interventions
- PROCEDURE
-
Endoscopic retrograde cholangiopancreatography (ERCP)
Patients with a clinical indication of Endoscopic retrograde cholangiopancreatography (ERCP) will be included in the study for malignant or benign conditions as in routine clinical practice. Endoscopic and radiological images of the second portion of the duodenum will be captured for each patient. In patients undergoing endoscopic ultrasound (EUS), additional images of the second portion of the duodenum at the level of the pancreatic head and common bile duct (CBD) will be obtained
Sponsors & Collaborators
-
University of La Laguna
lead OTHER
Eligibility
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2026-02-01
- Primary Completion
- 2026-12-31
- Completion
- 2027-03-31
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