Intervention time prediction from surgical low-level tasks

  • Authors:

  • Stefan Franke;JüRgen Meixensberger;Thomas Neumuth

  • Affiliations:

  • University of Leipzig, Innovation Center Computer Assisted Surgery, Leipzig, Germany;University of Leipzig, Innovation Center Computer Assisted Surgery, Leipzig, Germany and University Hospital Leipzig, Department of Neurosurgery, Leipzig, Germany;University of Leipzig, Innovation Center Computer Assisted Surgery, Leipzig, Germany

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Objective: Effective time and resource management in the operating room requires process information concerning the surgical procedure being performed. A major parameter relevant to the intraoperative process is the remaining intervention time. The work presented here describes an approach for the prediction of the remaining intervention time based on surgical low-level tasks. Materials and methods: A surgical process model optimized for time prediction was designed together with a prediction algorithm. The prediction accuracy was evaluated for two different neurosurgical interventions: discectomy and brain tumor resections. A repeated random sub-sampling validation study was conducted based on 20 recorded discectomies and 40 brain tumor resections. Results: The mean absolute error of the remaining intervention time predictions was 13min 24s for discectomies and 29min 20s for brain tumor removals. The error decreases as the intervention progresses. Discussion: The approach discussed allows for the on-line prediction of the remaining intervention time based on intraoperative information. The method is able to handle demanding and variable surgical procedures, such as brain tumor resections. A randomized study showed that prediction accuracies are reasonable for various clinical applications. Conclusion: The predictions can be used by the OR staff, the technical infrastructure of the OR, and centralized management. The predictions also support intervention scheduling and resource management when resources are shared among different operating rooms, thereby reducing resource conflicts. The predictions could also contribute to the improvement of surgical workflow and patient care.