  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.
