Development, deployment and scaling of operating room-ready artificial intelligence for real-time surgical decision support.

ABSTRACT: Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous ("Go"/"No-Go") zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test thi

SUBMITTER: Protserov S 

PROVIDER: S-EPMC11372100 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature