Clinical Research Directory

A Convolutional Neural Network for Difficult Biliary Cannulation

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. .

Prevalence and Predictive Factors of Difficult Biliary Cannulation

The main purpose of the study is to evaluate the prevalence of difficult biliary cannulation using endoscopic retrograde cholangiopancreatography (ERCP) according to the criteria defined by the European Society of Gastrointestinal Endoscopy (ESGE) and the American Society for Gastrointestinal Endoscopy (ASGE), as well as the predictive factors for this event in a prospective, multicenter study. Consecutive patients undergoing ERCP will be included in the study We will also assess: Evaluate the failure of biliary cannulation in our setting. Evaluate predictive factors for difficult cannulation and cannulation failure using ESGE criteria. Design an "a priori" predictive model for difficult cannulation and cannulation failure. Cost study: record of materials used.