Protocol for the Development of Artificial Intelligence Models for the Reduction of Surgical Complications Based on Intraoperative Video - Surg_Cloud project

Introduction Complications following abdominal surgery have a very significant negative impact on the patient and the health care system. Despite the spread of minimally invasive surgery, there is no automated way to use intraoperative video to predict complications. New developments in data storage capacity and artificial intelligence algorithm creation now allow for this opportunity. Methods Development of deep learning algorithms through supervised learning based on the Clavien-Dindo scale to categorise postoperative outcomes in minimally invasive abdominal surgery. An open-source dataset will be built, which will not only include intraoperative variables but also data related to patient outcomes, making it more generalisable and useful to the scientific community. This dataset will be shared under a non-commercial use license to promote scientific collaboration and innovation. Expected Results The planned outputs include the publication of a research protocol, main results, and the open-source dataset. Through this initiative, the project seeks to significantly advance the field of artificial intelligence-assisted surgery, contributing to safer and more effective practice.