Analysis of Executional and Procedural Errors in Dry-lab Robotic Surgery Experiments

TL;DR

This paper develops a rubric to identify executional and procedural errors in dry-lab robotic surgery tasks, analyzing their impact on performance and safety, and providing insights for automated error detection and training improvement.

Contribution

It introduces a task-specific error identification framework and analyzes kinematic and video data to distinguish error modes in robotic surgical demonstrations.

Findings

Error frequency varies by task and skill level.

Certain error modes are distinguishable by kinematic parameters.

Procedural errors correlate with lower scores and longer times.

Abstract

Background Analyzing kinematic and video data can help identify potentially erroneous motions that lead to sub-optimal surgeon performance and safety-critical events in robot-assisted surgery. Methods We develop a rubric for identifying task and gesture-specific Executional and Procedural errors and evaluate dry-lab demonstrations of Suturing and Needle Passing tasks from the JIGSAWS dataset. We characterize erroneous parts of demonstrations by labeling video data, and use distribution similarity analysis and trajectory averaging on kinematic data to identify parameters that distinguish erroneous gestures. Results Executional error frequency varies by task and gesture, and correlates with skill level. Some predominant error modes in each gesture are distinguishable by analyzing error-specific kinematic parameters. Procedural errors could lead to lower performance scores and increased demonstration times but also depend on surgical style. Conclusions This study provides insights into context-dependent errors that can be used to design automated error detection mechanisms and improve training and skill assessment.