Development of an Artificial Intelligence Tool for Intraoperative Guidance During Endovascular Aneurysm Repair

Abstract

Adverse events during surgery can occur in part owing to errors in visual perception and judgment. Deep learning is a branch of artificial intelligence (AI) that has shown promise to provide real-time intraoperative guidance and augment surgical performance. This study aims to train and test the performance of a deep learning model that can identify inappropriate landing zones on fluoroscopy during endovascular aneurysm repair (EVAR).