Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice.
View Article and Find Full Text PDFIntroduction: Cystic pancreatic neoplasms (CPN) are frequently diagnosed due to better diagnostic techniques and patients becoming older. However, diagnostic accuracy of endoscopic ultrasound (EUS) and value of follow-up are still unclear.
Material And Methods: The aim of our retrospective study was to investigate the frequency of different cystic pancreatic neoplasms (intraductal papillary mucinous neoplasm [IPMN], serous and mucinous cystadenoma, solid pseudopapillary neoplasia), diagnostic accuracy, size progression, and rate of malignancy using EUS in a tertiary reference center in Germany.
Background: Adenoma detection rate (ADR) varies significantly between endoscopists, with adenoma miss rates (AMRs) up to 26 %. Artificial intelligence (AI) systems may improve endoscopy quality and reduce the rate of interval cancer. We evaluated the efficacy of an AI system in real-time colonoscopy and its influence on AMR and ADR.
View Article and Find Full Text PDFObjective: Percutaneous biliary interventions (PBIs) can be associated with a high patient radiation dose, which can be reduced when national diagnostic reference levels (DRLs) are kept in mind. The aim of this multicentre study was to investigate patient radiation exposure in different percutaneous biliary interventions, in order to recommend national DRLs.
Methods: A questionnaire asking for the dose area product (DAP) and the fluoroscopy time (FT) in different PBIs with ultrasound- or fluoroscopy-guided bile duct punctures was sent to 200 advanced care hospitals.