摘要
Pancreatic diseases have a substantial burden on society which is predicted to increase further over the next decades.Endoscopic ultrasonography(EUS)remains the best available diagnostic method to assess the pancreas,however,there remains room for improvement.Artificial intelligence(AI)approaches have been adopted to assess pancreatic diseases for over a decade,but this methodology has recently reached a new era with the innovative machine learning algorithms which can process,recognize,and label endosonographic images.Our review provides a targeted summary of AI in EUS for pancreatic diseases.Included studies cover a wide spectrum of pancreatic diseases from pancreatic cystic lesions to pancreatic masses and diagnosis of pancreatic cancer,chronic pancreatitis,and autoimmune pancreatitis.For these,AI models seemed highly successful,although the results should be evaluated carefully as the tasks,datasets and models were greatly heterogenous.In addition to use in diagnostics,AI was also tested as a procedural real-time assistant for EUS-guided biopsy as well as recognition of standard pancreatic stations and labeling anatomical landmarks during routine examination.Studies thus far have suggested that the adoption of AI in pancreatic EUS is highly promising and further opportunities should be explored in the field.
