A Convolutional Neural Network for Difficult Biliary Cannulation

ConditionDifficult Biliary Cannulation

Trial statusNot yet recruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

AgeNot listed

SponsorUniversity of La Laguna

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Age >18 years

Signed informed consent

Patients indicated for ERCP

Disqualifiers

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)

Trial population

All consecutive patients who meet the inclusion criteria and none of the exclusion criteria will be offered participation in the study.

Trial design

Design model

Other

Time perspective

Cross-sectional

Treatments tested in this trial

Endoscopic retrograde cholangiopancreatography (ERCP)
Procedure/Surgery
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

Treatment groups

600 Participants

are divided into 1 treatment group

Group A: Patients undergoing ERCP1 intervention

Trial outcomes

Primary outcomes

To train a convolutional neural network to predict difficult biliary canulation

To train a convolutional neural network to predict difficult biliary canulation following the European Society of Gastrointestinal Endoscopy Society (ESGE): Assessment of difficult biliary cannulation defined by ESGE criteria (\> 5 minutes duration until cannulation, \> 5 cannulation attempts, \> 1 passage of the guidewire into the main pancreatic duct). Based on these criteria pictures will be labelled as difficult biliary cannulation or not

Time frame

1 year

Secondary outcomes

A validation of the CNN

A validation of the CNN assessing the agreement between ESGE criteria and the CNN prediction

Time frame

1 year

Locations

This trial has no locations.

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

University of La Laguna

Lead sponsor

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: