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The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.
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Acute pancreatitis is associated with significant morbidity and mortality. It can develop complications such as fluid collections and necrosis. Infection of necrosis occurs in about 20-40% of patients with severe acute pancreatitis, and is associated with organ failure and worse prognosis. In the past years, the treatment of pancreatic collections has shifted from open surgery to minimally invasive techniques, such as endoscopic ultrasound guided drainage. These guidelines from a selection of experts among the Endoscopic Ultrasound Group from the Spanish Society of Gastrointestinal Endoscopy (GSEED-USE) have the purpose to provide advice on the management of pancreatic collections based on a thorough review of the available scientific evidence. It also reflects the experience and clinical practice of the authors, who are advanced endoscopists or clinical pancreatologists with extensive experience in managing patients with acute pancreatitis.
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Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.
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Campylobacter infection usually starts in the jejunum and ileum and progresses distally. The case fatality rate is low and most occur in elderly or patients with comorbidity as in this case. Antibiotics should be used in severe cases or patients at risk. The choices are macrolides and fluoroquinolones. However, in some countries quinolone resistance is increasing, as in Spain. We shouldn´t forget this fact for the proper treatment approach and specifically in refractory cases.