INTRODUCTION Objective quantification of technical surgical skill in surgery remains poorly defined although delivery of and training in these skills is essential to the surgical profession. Attempts to measure hand kinematics to quantify surgical performance primarily rely on electromagnetic sensors attached to the surgeon’s hand or instrument. We sought to determine whether similar motion analysis could be performed using a marker-less video-based review, allowing for a scalable approach to performance evaluation. METHODS We recorded six reduction mammoplasty operations – a plastic surgery procedure in which the attending and resident surgeons operate in parallel. Segments representative of surgical tasks were identified using Multimedia Video Task Analysis software. Video digital processing was used to extract and analyze the spatio-temporal characteristics of hand movement. RESULTS Attending plastic surgeons appear to use their non-dominant hand more than residents when cutting with the scalpel, suggesting more use of counter-traction. While suturing, attendings were more ambidextrous with smaller differences in movement between their dominant and non-dominant hands than residents. Attendings also seem to have more conservation of movement when performing instrument tying than residents, as demonstrated by less non-dominant hand displacement. These observations were consistent within procedures and between the different attending plastic surgeons evaluated in this fashion. CONCLUSIONS Video motion analysis can be used to provide objective measurement of technical skills without the need for sensors or markers. Such data should be valuable in better understanding the acquisition and degradation of surgical skill, providing enhanced feedback to shorten the learning curve.

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.

We are developing video processing algorithms for automatically measuring the ACGIH TLV ® hand activity level (HAL) using marker-less tracking of hand movements. An equation for computing HAL ratings directly from tracked kinematics, rather than using a frequency-duty cycle (DC) look-up table, more readily lends itself to automated processing. Videos from the 33 Latko et al. (1997) jobs were digitized and analyzed using marker-less tracking, and hand root mean square (RMS) speed (S) was measured. A linear regression model was developed for predicting the average observer rated HAL based on the measured hand RMS speed and DC. Since the videos did not contain distance calibration, speed was quantified in pixels/s and normalized by the distance of each worker's hand breadth, measured in pixels. A Monte Carlo simulation was performed using the US Army (1991) hand anthropometry data to determine how variation is introduced in the equation as hand breadth varies. The resulting equation was HAL= −1.06 + 0.0047 S + 0.053 DC and it predicted HAL ratings within ±1. The development of an accurate equation for estimating HAL ratings should enable use of automated and objective measurement in practice. While expert observer HAL ratings offer speed and efficiency, use of objective measurements based on worker hand kinematics should provide greater reliability, as well as offering specific engineering aspects of the job that may be addressed for reducing exposures and the risk of musculoskeletal disorders. Furthermore, automated videos analysis may help improve the speed and efficiency of making objective measurements in practice. BACKGROUND