Offline identification of surgical deviations in laparoscopic rectopexy

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 crossvalidation.Results: The best results have over 90 % accuracy. Recall and precision were superior at 70 %. We have provided a detailed analysis of the incorrectlydetected 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.

Explaining a model predicting quality of surgical practice: a first presentation to and review by clinical experts

“…A majority of studies relating to surgical practice work with practice indicators. Only a few recent studies have exploited laparoscopic videos with the aim of better understanding clinical practice: [6] proposed to automatically detect intra-operative adverse events based on the annotation of surgical activities and hidden semi-Markov models, but the image content was not exploited. [11] used Convolutional Neural Networks (CNNs) to automatically assess the critical view of safety in laparoscopic cholecystectomy, but the use of a CNN makes it challenging to interpret the network's predictions.…”

Temporal Memory Relation Network for Workflow Recognition from Surgical Video

“…the surgical workflow is a fundamental enabler to design intelligent assistant systems in the operating room [2]. Particularly, workflow recognition enables the systems to monitor and optimize surgical process, provide context-aware decision support, and generate early warning of potential deviations and anomalies [3]. The real-time analysis of workflow information can facilitate coordination and communication among the surgical team members [4].…”

MIcro-Surgical Anastomose Workflow recognition challenge report

“…A SPM is defined as a "simplified pattern of a surgical process that reflects a predefined subset of interest of the surgical process in a formal or semi-formal representation" [1]. The SPM methodology is used for various applications, such as operating room optimization and management [2,3], learning and expertise assessment [4,5], robotic assistance [6], decision support [7], and quality supervision [8].…”

“…In early publications [2,3,4,5,6,7,8,10], SPMs were manually acquired by human observers. However, this solution has several drawbacks: It is costly concerning human resources, time-consuming, observer-dependent, and errors could be made.…”