Utilizing Grasp Monitoring to Predict Microsurgical Expertise

Abstract

IntroductionMost microsurgical procedures require the surgeon to use tools to grasp and hold fragile objects in the surgical site. Prior research on grasping in surgery has mostly either been in other surgical techniques or used grasping as an auxiliary metric. We focus on microsurgery and investigate what grasping can tell about microsurgical skill and suturing performance. This study lays groundwork for using automatic detection of grasps to evaluate surgical skill. MethodsFive expert surgeons and six novices completed sutures on a microsurgical training board. Video recordings of the performance were annotated for the number of grasps, while an eye tracker recorded the participants’ pupil dilations for cognitive workload assessment. Performance was measured with suturing duration and the University of Western Ontario Microsurgical Skills Assessment instrument (UWOMSA). Differences in skill, suturing performance and cognitive workload were compared with grasping behavior. ResultsNovices needed significantly more grasps to complete sutures and failed to grasp more often than the experts. The number of grasps affected the suturing duration more in novices. Decreasing suturing efficiency as measured by UWOMSA instrument was associated with increase in grasps, even when we controlled for overall skill differences. Novices displayed larger pupil dilations when averaged over a sufficiently large sample, and the difference increased after the grasp. ConclusionsGrasping action during microsurgical procedures can be used as a conceptually simple yet objective proxy in microsurgical performance assessment. If the grasps could be detected automatically, they could be used to aid in computational evaluation of surgical trainees’ performance.