Arnaud Huaulmé1, Pierre Jannin2, Fabian Reche3, Jean-Luc Faucheron3, Alexandre Moreau-Gaudry4, Sandrine Voros5. 1. UGA/CNRS/INSERM, TIMC-IMAG UMR 5525, Grenoble F-38041, France; Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F35000, France. 2. Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F35000, France. Electronic address: pierre.jannin@univ-rennes1.fr. 3. Colorectal Unit, Department of Surgery, Michallon University Hospital, Grenoble F-38043, France. 4. UGA/CNRS/INSERM, TIMC-IMAG UMR 5525, Grenoble F-38041, France; UGA/CHU Grenoble/INSERM, Centre d'Investigation Clinique - Innovation Technologique, CIT803, Grenoble F-38041, France. 5. UGA/CNRS/INSERM, TIMC-IMAG UMR 5525, Grenoble F-38041, France.
Abstract
OBJECTIVE: According to a meta-analysis of 7 studies, the median number of patients with at least one adverse event during the surgery is 14.4%, and a third of those adverse events were 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 for event deviations, i.e. related to adverse events, are respectively below 80% and 40%. To understand these results, 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.
OBJECTIVE: According to a meta-analysis of 7 studies, the median number of patients with at least one adverse event during the surgery is 14.4%, and a third of those adverse events were 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 for event deviations, i.e. related to adverse events, are respectively below 80% and 40%. To understand these results, 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.