Background
Quantitative analysis of technical skill relies largely on specially-tagged instruments or tracers on surgeons’ hands, often in simulated settings. We investigated a novel, marker-less technique for evaluating technical skill during open surgeries, and differentiating tasks and surgeon experience level.
Methods
We recorded the operative field via in-light camera for open operations. Sixteen cases yielded 138 video clips of suturing and tying tasks ≥5 seconds in length. Video clips were categorized based on surgeon role (attending, resident) and task sub-type (suturing tasks: Body Wall, Bowel Anastomosis, Complex Anastomosis; tying tasks: Body Wall, Superficial Tying; Deep Tying). We tracked a region of interest on the hand to generate kinematic data. Nested multi-level modeling addressed the non-independence of clips obtained from the same surgeon.
Results
Interaction effects for suturing tasks were seen between role and task categories for average speed (p=0.04), standard deviation of speed (p=0.05), and average acceleration (p=0.03). There were significant differences across task categories for standard deviation of acceleration (p=0.02). Significant differences for tying tasks across task categories were observed for maximum speed (p=0.02), standard deviation of speed (0.04), and average (p=0.02), maximum (p<0.01), and standard deviation (p=0.03) of acceleration.
Conclusions
We demonstrated the ability to detect kinematic differences in performance using marker-less tracking during open surgical cases. Suturing task evaluation was most sensitive to differences in surgeon role and task category and may represent a scalable approach to provide quantitative feedback to surgeons about technical skill.