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Offline identification of surgical deviations in laparoscopic rectopexy.
- Source :
-
Artificial intelligence in medicine [Artif Intell Med] 2020 Apr; Vol. 104, pp. 101837. Date of Electronic Publication: 2020 Feb 27. - Publication Year :
- 2020
-
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.<br />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.<br />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.<br />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.<br />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.<br /> (Copyright © 2020 Elsevier B.V. All rights reserved.)
- Subjects :
- Computer Systems
Humans
Workflow
Laparoscopy
Surgeons
Subjects
Details
- Language :
- English
- ISSN :
- 1873-2860
- Volume :
- 104
- Database :
- MEDLINE
- Journal :
- Artificial intelligence in medicine
- Publication Type :
- Academic Journal
- Accession number :
- 32499005
- Full Text :
- https://doi.org/10.1016/j.artmed.2020.101837