Artificial Intelligence Vision Based Analysis and Key Steps Identification Feasibility Study in Laparoscopic Hysterectomies

Abstract

<h3>Study Objective</h3> The analysis of surgical videos and key steps identification using artificial intelligence (AI) holds great promise for the future of healthcare. In this paper, we present a novel computer vision algorithm for surgical steps identification in laparoscopic hysterectomy. <h3>Design</h3> A retrospective analysis of surgical videos of laparoscopic hysterectomies. <h3>Setting</h3> Gynecological department in a tertiary hospital with an average of 6500 procedures a year. <h3>Patients or Participants</h3> 190 laparoscopic hysterectomies from September 2020 to April 2022. <h3>Interventions</h3> Artificial intelligence-driven surgical platform that uses advanced computer vision technology to capture video data during all surgeries, de-identify it, and upload it to a secure cloud infrastructure. <h3>Measurements and Main Results</h3> A total of 190 full-length hysterectomies videos were manually annotated with sequential steps of surgery. Of these, 115 cases served as a training dataset for algorithm development, 28 cases were used for internal validation, and 47 were used as a separate testing cohort for evaluating algorithm accuracy. Concordance between AI-enabled automated video analysis and manual human video annotation was 92%. Algorithm accuracy was highest for the vaginal vault closure step (97%) and lowest for the adhesiolysis step (71%). <h3>Conclusion</h3> A variety of AI applications are expanding in clinical systems, from databases to intraoperative video analysis. The unique nature of surgical practice puts surgeons in a strong position to contribute to the next phase of AI, focused on real-time clinical decision support designed to optimize surgeon workflow and patient care. This work validates the ability to annotate the different steps of hysterectomy using an AI vision-based algorithm. Automated surgical video analysis has immediate impact in this field and the implementation of such a system holds enormous promise for future surgical education and improvement.