A Novel Dissection Gesture Classification to Characterize Robotic Dissection Technique for Renal Hilar Dissection

No AccessJournal of UrologyNew Technology and Techniques1 Jan 2021A Novel Dissection Gesture Classification to Characterize Robotic Dissection Technique for Renal Hilar Dissection Runzhuo Ma, Erik B. Vanstrum, Jessica H. Nguyen, Andrew Chen, Jian Chen, and Andrew J. Hung Runzhuo MaRunzhuo Ma Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California , Erik B. VanstrumErik B. Vanstrum Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California , Jessica H. NguyenJessica H. Nguyen Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California , Andrew ChenAndrew Chen Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California , Jian ChenJian Chen Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California , and Andrew J. HungAndrew J. Hung *Correspondence: University of Southern California Institute of Urology, 1441 Eastlake Ave., Suite 7416, Los Angeles, California 90089 telephone: 323-865-3700; FAX: 323-865-0120; E-mail Address: [email protected] Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California View All Author Informationhttps://doi.org/10.1097/JU.0000000000001328AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract 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. References 1. : The effect of technical performance on patient outcomes in surgery: a systematic review. Ann Surg 2017; 265: 492. Google Scholar 2. : Surgical skill and complication rates after bariatric surgery. N Engl J Med 2013; 369: 1434. Google Scholar 3. : Surgical process modelling: a review. Int J Comput Assist Radiol Surg 2014; 9: 495. Google Scholar 4. : Video content analysis of surgical procedures. Surg Endosc 2018; 32: 553. Google Scholar 5. : Structured learning for robotic surgery utilizing a proficiency score: a pilot study. World J Urol 2017; 35: 27. Google Scholar 6. : Use of automated performance metrics to measure surgeon performance during robotic vesicourethral anastomosis and methodical development of a training tutorial. J Urol 2018; 200: 895. Link, Google Scholar 7. : Crowdsourcing assessment of surgeon dissection of renal artery and vein during robotic partial nephrectomy: a novel approach for quantitative assessment of surgical performance. J Endourol 2016; 30: 447. Google Scholar 8. : Learning curves for urological procedures: a systematic review. BJU Int 2014; 114: 617. Google Scholar 9. : Development and validation of objective performance metrics for robot-assisted radical prostatectomy: a pilot study. J Urol 2018; 199: 296. Link, Google Scholar 10. : Experts vs super-experts: differences in automated performance metrics and clinical outcomes for robot-assisted radical prostatectomy. BJU Int 2019; 123: 861. Google Scholar No direct or indirect commercial, personal, academic, political, religious or ethical incentive is associated with publishing this article. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsCited ByGhodoussipour S, Reddy S, Ma R, Huang D, Nguyen J and Hung A (2021) Reply by AuthorsJournal of Urology, VOL. 205, NO. 5, (1302-1302), Online publication date: 1-May-2021.Vanstrum E, Ma R, Maya-Silva J, Sanford D, Nguyen J, Lei X, Chevinksy M, Ghoreifi A, Han J, Polotti C, Powers R, Yip W, Zhang M, Aron M, Collins J, Daneshmand S, Davis J, Desai M, Gerjy R, Goh A, Hu J, Kimmig R, Lendvay T, Porter J, Sotelo R, Sundaram C, Cen S, Gill I and Hung A (2021) Development and Validation of an Objective Scoring Tool to Evaluate Surgical Dissection: Dissection Assessment for Robotic Technique (DART)Urology Practice, VOL. 8, NO. 5, (596-604), Online publication date: 1-Sep-2021. Volume 205Issue 1January 2021Page: 271-275 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.KeywordsnephrectomydissectionsurgeonseducationroboticsAcknowledgementsShubham Bhatia assisted with data analysis. Daniel Sanford and Balint Der provided technical support of video editing.MetricsAuthor Information Runzhuo Ma Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California More articles by this author Erik B. Vanstrum Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California More articles by this author Jessica H. Nguyen Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California More articles by this author Andrew Chen Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California More articles by this author Jian Chen Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California More articles by this author Andrew J. Hung Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California *Correspondence: University of Southern California Institute of Urology, 1441 Eastlake Ave., Suite 7416, Los Angeles, California 90089 telephone: 323-865-3700; FAX: 323-865-0120; E-mail Address: [email protected] Financial interest and/or other relationship with Quantgene, Inc. and Mimic Technologies, Inc. More articles by this author Expand All No direct or indirect commercial, personal, academic, political, religious or ethical incentive is associated with publishing this article. Advertisement Loading ...