Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
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Gastrointestinal endoscopy is fundamental to diagnostic and therapeutic procedures in pediatric gastroenterology. In the decades since endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS) for hepatobiliary and pancreatic disease were introduced into clinical practice, there has been increasing interest in these procedures, and practice guidelines and position papers that clearly define the role of ERCP and EUS in children have been published. Based on the distinction of endoscopy between children and adults, this review focuses on the current state of ERCP and EUS procedures in children, including the types of endoscopes used in children, general anesthesia and radiation exposure, biliary and pancreatic indications, considerations of education and training for ERCP and EUS procedures in children, and expectations for development of endoscopes for children.
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Endoscopic ultrasonography (EUS) is an essential diagnostic tool for various types of pancreatic diseases such as pancreatic tumors and chronic pancreatitis; however, EUS imaging has low specificity for the diagnosis of pancreatic diseases. Artificial intelligence (AI) is a mathematical prediction technique that automates learning and recognizes patterns in data. This review describes the details and principles of AI and deep learning algorithms. ⋯ For this, conventional machine learning architectures are used, and deep learning architecture has been used in only two reports. Although the diagnostic abilities in these reports were about 85-95%, these were exploratory research and very few reports have included substantial evidence. AI is increasingly being used for medical image diagnosis due to its high performance and will soon become an essential technique for medical diagnosis.
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Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. ⋯ We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.
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All gastrointestinal endoscopic procedures have a high risk of aerosol contamination of the coronavirus disease 2019 (COVID-19) to endoscopists, nurses, and healthcare assistants. Given the current pandemic situation of COVID-19, the Japan Gastroenterological Endoscopy Society issued the recommendation for gastrointestinal (GI) endoscopy based on the status of COVID-19 as of April 9, 2020, in Japan: (i) indications for GI endoscopy in the pandemic of COVID-19; (ii) practical protective equipment for medical personnel depending on the risk for COVID-19; (iii) preprocedural management, such as pharyngeal local anesthesia using lidocaine spray which has a potential to generate the aerosols; (iv) ideal settings of the endoscopy room including the numbers of the staff and the patients; (v) postprocedural management, such as undressing and follow-up of the patients, as well as the involved staff, were documented to fit the practical scenarios in GI endoscopy, with the available data in Japan and the world. We believe that certain measures will prevent further spread of COVID-19.