Smoothness of Surgical Tool Tip Motion Correlates to Skill in Endovascular Tasks

Sean Estrada, Cassidy Duran, Daryl Schulz, Jean Bismuth, Michael D. Byrne, Marcia K. Omalley

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

Current performance assessment techniques in endovascular surgery are subjective or limited to grading scales based solely on an expert's observation of a novice's task execution. Since most endovascular procedures involve performing fine motor control tasks that require complex dexterous movements, this paper evaluates objective and quantitative metrics of performance that capture movement quality through the computation of tool tip movement smoothness. An experiment was designed that involved recording the catheter tip movement from 20 subjects performing four fundamental endovascular tasks in each of three sessions using manual catheterization on a physical model and in a simulation environment. Several motion-based performance measures that have been shown to reliably assess skill in other domains were computed and tested for correlation with subjective data that were simultaneously obtained from the global rating scale assessment tool. Metrics that captured movement smoothness produced statistically significant correlations with the observation-based assessment metrics and were able to differentiate skill among participants. In particular, submovement analysis led to metrics that captured statistically significant differences across ability group, session, experimental platform, and task. Objective and quantitative metrics that capture movement smoothness could be incorporated into future training protocols to provide detailed feedback on trainee performance.

Original languageEnglish (US)
Article number7462178
Pages (from-to)647-659
Number of pages13
JournalIEEE Transactions on Human-Machine Systems
Volume46
Issue number5
DOIs
StatePublished - Oct 2016

Keywords

  • Motion capture
  • skill assessment
  • surgical training
  • virtual reality

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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