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Purpose: Evaluation of surgical competency has important implications for training new surgeons, accreditation, and improving patient outcomes. A method to specifically evaluate dissection performance does not yet exist. This project aimed to design a tool to assess surgical dissection quality., Methods: Delphi method was used to validate structure and content of the dissection evaluation. A multi-institutional and multi-disciplinary panel of 14 expert surgeons systematically evaluated each element of the dissection tool. Ten blinded reviewers evaluated 46 de-identified videos of pelvic lymph node and seminal vesicle dissections during the robot-assisted radical prostatectomy. Inter-rater variability was calculated using prevalence-adjusted and bias-adjusted kappa. The area under the curve from receiver operating characteristic curve was used to assess discrimination power for overall DART scores as well as domains in discriminating trainees (≤100 robotic cases) from experts (>100)., Results: Four rounds of Delphi method achieved language and content validity in 27/28 elements. Use of 3- or 5-point scale remained contested; thus, both scales were evaluated during validation. The 3-point scale showed improved kappa for each domain. Experts demonstrated significantly greater total scores on both scales (3-point, p < 0.001; 5-point, p < 0.001). The ability to distinguish experience was equivalent for total score on both scales (3-point AUC= 0.92, CI 0.82-1.00, 5-point AUC= 0.92, CI 0.83-1.00)., Conclusions: We present the development and validation of Dissection Assessment for Robotic Technique (DART), an objective and reproducible 3-point surgical assessment to evaluate tissue dissection. DART can effectively differentiate levels of surgeon experience and can be used in multiple surgical steps., Competing Interests: Conflicts of interest/Competing interests: M Aron has financial disclosures with Intuitive. JW Davis is a consultant for Intuitive Surgical and Janssen. MM Desai is a consultant for Auris Health, PROCEPT BioRobotics. AC Goh is a consultant for Medtronic. R Kimmig has financial disclosures with Intuitive Surgical Inc., Medtronic, Avatera, CMR and Medicaroid. J Porter is a consultant for Medtronic, Ceevra, Proximie, Intuitive. I Gill is an unpaid advisor for Steba Biotech. AJ Hung is a consultant for Mimic Technologies, Quantagene, and Johnson & Johnson. The study was not funded by any of these companies. Other authors have no conflict of interest.

Purpose: Deconstruction of robotic surgical gestures into semantic vocabulary yields an effective tool for surgical education. In this study we disassembled tissue dissection into basic gestures, created a classification system, and showed its ability to distinguish between experts and novices., Materials and Methods: Videos of renal hilum preparation during robotic assisted partial nephrectomies were manually reviewed to identify all discrete surgical movements. Identified dissection movements were classified into distinct gestures based on the consensus of 6 expert surgeons. This classification system was then employed to compare expert and novice dissection patterns during the renal hilum preparation., Results: A total of 40 robotic renal hilum preparation videos were reviewed, representing 16 from 6 expert surgeons (100 or more robotic cases) and 24 from 13 novice surgeons (fewer than 100 robotic cases). Overall 9,819 surgical movements were identified, including 5,667 dissection movements and 4,152 supporting movements. Nine distinct dissection gestures were identified and classified into the 3 categories of single blunt dissection (spread, peel/push, hook), single sharp dissection (cold cut, hot cut and burn dissect) and combination gestures (pedicalize, 2-hand spread, and coagulate then cut). Experts completed 5 of 9 dissection gestures more efficiently than novices (p ≤0.033). In consideration of specific anatomical locations, experts used more peel/push and less hot cut while dissecting the renal vein (p <0.001), and used more pedicalize while dissecting the renal artery (p <0.001)., Conclusions: Using this novel dissection gesture classification system, key differences in dissection patterns can be found between experts/novices. This comprehensive classification of dissection gestures may be broadly applied to streamline surgical education.