|Authors||Frasier LL, Azari DP, Ma Y, Pavuluri Quamme SR, Radwin RG, Pugh CM, Yen TY, Chen CH, Greenberg CC|
|Journal||Surgery Volume: 160 Issue: 5 Pages: 1400-1413|
|Publish Date||2016 Nov|
Often in simulated settings, quantitative analysis of technical skill relies largely on specially tagged instruments or tracers on surgeons’ hands. We investigated a novel, marker-less technique for evaluating technical skill during open operations and for differentiating tasks and surgeon experience level.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 duration. Video clips were categorized based on surgeon role (attending, resident) and task subtype (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, multilevel modeling addressed the nonindependence of clips obtained from the same surgeon.Interaction effects for suturing tasks were seen between role and task categories for average speed (P = .04), standard deviation of speed (P = .05), and average acceleration (P = .03). There were significant differences across task categories for standard deviation of acceleration (P = .02). Significant differences for tying tasks across task categories were observed for maximum speed (P = .02); standard deviation of speed (P = .04); and average (P = .02), maximum (P < .01), and standard deviation (P = .03) of acceleration.We demonstrated the ability to detect kinematic differences in performance using marker-less tracking during open operative cases. Suturing task evaluation was most sensitive to differences in surgeon role and task category and may represent a scalable approach for providing quantitative feedback to surgeons about technical skill.
|Full Text||Full text available on PubMed Central|