713 The Application of Artificial Intelligence for Digital Imaging in the Operating Theatre: A Systematic Review and Narrative Synthesis

Abstract

Abstract Introduction Promising applications of artificial intelligence (AI) in healthcare are emerging. This systematic review aims to identify and synthesise applications of digital-imaging AI in surgery and inform future work. Method Systematic database searches (Medline, Embase, CENTRAL) were undertaken. Studies concerning digital-imaging AI within the operating theatre were identified from title and abstract screening. Selection was further refined to identify video-based AI models with direct supportive output to the surgeon within the operating theatre. Results 48 studies were included. Studies spanned 13 specialty groupings, with n=42 utilising a pre-specified dataset and the remaining n=6 using AI with human participants. The most common field using AI was urology (n=9 studies). Applications were most commonly for navigation and visualisation support (n=26 studies across 10 surgical specialties) and AI-based intelligent detection systems, intended to identify and highlight useful surgical information using computer-vision pattern recognition (n=18 articles across n=6 specialties). Other applications included video-processing algorithms (n=3 studies across 2 specialties), and a novel imaging modality for visualising blood perfusion (n=1 study), proposing operating theatre-based application. High-performance models were identified across a range of pathologies. This manifested as minimal overlay errors and acceptable frame rates for navigation tools, and high diagnostic performance for detection systems (determined by area-under-the-receiver-operating-characteristic-curve, sensitivity/specificity, negative/positive predictive values). Conclusions There is evidence to suggest AI for intraoperative surgeon-support has potential, particularly through augmented-reality navigation and AI-enabled information awareness. Further research and optimisations are required to produce clinically robust models, which remain high-performance despite case variability. Such AI may support improved surgical access, efficiency, and outcomes.