Enhancing detection of various pancreatic lesions on endoscopic ultrasound through artificial intelligence: a basis for computer‐aided detection systems

Endoscopic ultrasound (EUS) is the most sensitive method for evaluation of pancreatic lesions but is limited by significant operator dependency. Artificial intelligence (AI), in the form of computer-aided detection (CADe) systems, has shown potential in increasing accuracy and bridging operator dependency in several endoscopic domains. However, the complexity of integrating AI into EUS is far more challenging. This aims to develop and test the basis for a CADe system for real-time detection and segmentation of all pancreatic lesions.