Surgical skills: Can learning curves be computed from recordings of surgical activities?

Abstract

Purpose: Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets.

Methods: Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves.

Results: Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression.

Conclusion: Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.

Keywords: DTW; Learning curves; Surgical data science; Surgical process model.