Performance of artificial intelligence models for predicting intraoperative complications during surgery in real time: a systematic review and meta-analysis protocol.

Fiche publication

Date publication

octobre 2025

Journal

BMJ open

Auteurs

Membres identifiés du Cancéropôle Est :
Dr CHAUSSY Yann , Mr LIHOREAU Thomas

Tous les auteurs :
Bronnert R, Besch G, Hild O, Lihoreau T, Chaussy Y, Ferreira D

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/41120159

Résumé

Intraoperative complications contribute significantly to morbidity and mortality, and reducing their risk is a primary objective for all operating room's healthcare professionals. Many of these complications are predictable and could be anticipated by the surgeon or anaesthesiologist. Various clinical scores were developed to assess cardiovascular risk, acute kidney injury or acute respiratory failure preoperatively. However, these scores require time for calculation and are not designed to be adjusted in real time during surgery, based on physiological signals and new intraoperative events. Besides, some events remain unpredictable because they are multifactorial.In recent decades, Artificial Intelligence (AI)-based algorithms have been tested for the real-time prediction of intraoperative complications. These algorithms have the potential to continuously analyse patient data and provide early warnings, enabling professionals to intervene more effectively.The aim of this review is to address the question: 'What is the performance of AI models in predicting intraoperative complications during surgery using baseline and real-time data?'.