Offline identification of surgical deviations in laparoscopic rectopexy

TL;DR

This paper presents a novel method using hidden semi-Markov models and multi-dimensional temporal scaling to automatically identify deviations from standard surgical procedures in laparoscopic rectopexy, aiming to enhance patient safety.

Contribution

It introduces a new approach for detecting surgical process deviations based on non-linear temporal scaling and hidden semi-Markov models, specifically focusing on surgeon deviations rather than anatomical variations.

Findings

Achieved over 90% accuracy in deviation detection

Recall and precision exceeded 70%

Detailed error analysis suggests avenues for further improvement

Abstract

Objective: A median of 14.4% of patient undergone at least one adverse event during surgery and a third of them are preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Therefore, it is clear that the automatic identification of adverse events is a major challenge for patient safety. In this paper, we have proposed a method enabling us to identify such deviations. We have focused on identifying surgeons' deviations from standard surgical processes due to surgical events rather than anatomic specificities. This is particularly challenging, given the high variability in typical surgical procedure workflows. Methods: We have introduced a new approach designed to automatically detect and distinguish surgical process deviations based on multi-dimensional non-linear temporal scaling with a hidden semi-Markov model using manual annotation of surgical processes. The approach was then evaluated using cross-validation. Results: The best results have over 90% accuracy. Recall and precision were superior at 70%. We have provided a detailed analysis of the incorrectly-detected observations. Conclusion: Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model provides promising results for detecting deviations. Our error analysis of the incorrectly-detected observations offers different leads in order to further improve our method. Significance: Our method demonstrated the feasibility of automatically detecting surgical deviations that could be implemented for both skill analysis and developing situation awareness-based computer-assisted surgical systems.