Real-time AI-powered EUS imaging analysis software for detection and segmentation of cystic versus solid pancreatic lesions

Aims Detection and differentiation of cystic versus solid pancreatic lesions prior to performance of EUS-guided fine needle aspiration biopsy (EUS-FNA/B) can be automated by use of artificial intelligence (AI) techniques based on convolutional neural networks (CNN). We aimed to test the technical feasibility of a novel real-time AI-powered EUS imaging analysis software for detection and segmentation of the pancreas, cystic pancreatic lesions and/or solid pancreatic masses (PANC-AI).