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Innovations in Urologic Surgical Training

  • Surgery (M Desai, Section Editor)
  • Published:
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Abstract

Purpose of Review

This review aims to summarize innovations in urologic surgical training in the past 5 years.

Recent Findings

Many assessment tools have been developed to objectively evaluate surgical skills and provide structured feedback to urologic trainees. A variety of simulation modalities (i.e., virtual/augmented reality, dry-lab, animal, and cadaver) have been utilized to facilitate the acquisition of surgical skills outside the high-stakes operating room environment. Three-dimensional printing has been used to create high-fidelity, immersive dry-lab models at a reasonable cost. Non-technical skills such as teamwork and decision-making have gained more attention. Structured surgical video review has been shown to improve surgical skills not only for trainees but also for qualified surgeons.

Summary

Research and development in urologic surgical training has been active in the past 5 years. Despite these advances, there is still an unfulfilled need for a standardized surgical training program covering both technical and non-technical skills.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Hung AJ, Chen J, Ghodoussipour S, Oh PJ, Liu Z, Nguyen J, et al. A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int. 2019;124:487–95. https://doi.org/10.1111/bju.14735.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Goldenberg MG, Goldenberg L, Grantcharov TP. Surgeon performance predicts early continence after robot-assisted radical prostatectomy. J Endourol. 2017;31:858–63. https://doi.org/10.1089/end.2017.0284.

    Article  PubMed  Google Scholar 

  3. Aydin A, Raison N, Khan MS, Dasgupta P, Ahmed K. Simulation-based training and assessment in urological surgery. Nat Rev Urol. 2016;13:503–19. https://doi.org/10.1038/nrurol.2016.147A comprehensive review covering virtual/augmented reality, dry-lab, animal and cadaveric simulation models of urologic surgical training before 2016.

    Article  PubMed  Google Scholar 

  4. McDougall EM. Validation of surgical simulators. J Endourol. 2007;21:244–7. https://doi.org/10.1089/end.2007.9985.

    Article  PubMed  Google Scholar 

  5. Goldenberg M, Lee JY. Surgical education, simulation, and simulators-updating the concept of validity. Curr Urol Rep. 2018;19:52. https://doi.org/10.1007/s11934-018-0799-7.

    Article  PubMed  Google Scholar 

  6. McKendy KM, Watanabe Y, Lee L, Bilgic E, Enani G, Feldman LS, et al. Perioperative feedback in surgical training: a systematic review. Am J Surg. 2017;214:117–26. https://doi.org/10.1016/j.amjsurg.2016.12.014.

    Article  PubMed  Google Scholar 

  7. Timberlake MD, Mayo HG, Scott L, Weis J, Gardner AK. What do we know about intraoperative teaching?: a systematic review. Ann Surg. 2017;266:251–9. https://doi.org/10.1097/SLA.0000000000002131.

    Article  PubMed  Google Scholar 

  8. Vaidya A, Aydin A, Ridgley J, Raison N, Dasgupta P, Ahmed K. Current status of technical skills assessment tools in surgery: a systematic review. J Surg Res. 2020;246:342–78. https://doi.org/10.1016/j.jss.2019.09.006.

    Article  PubMed  Google Scholar 

  9. Hussein AA, Ghani KR, Peabody J, Sarle R, Abaza R, Eun D, et al. Development and validation of an objective scoring tool for robot-assisted radical prostatectomy: prostatectomy assessment and competency evaluation. J Urol. 2017;197:1237–44. https://doi.org/10.1016/j.juro.2016.11.100.

    Article  PubMed  Google Scholar 

  10. Hussein AA, Abaza R, Rogers C, Boris R, Porter J, Allaf M, et al. Development and validation of an objective scoring tool for minimally invasive partial nephrectomy: scoring for partial nephrectomy (SPAN). J Urol. 2018;199. https://doi.org/10.1016/j.juro.2018.02.442.

  11. Hussein AA, Sexton KJ, May PR, Meng MV, Hosseini A, Eun DD, et al. Development and validation of surgical training tool: cystectomy assessment and surgical evaluation (CASE) for robot-assisted radical cystectomy for men. Surg Endosc. 2018;32:4458–64. https://doi.org/10.1007/s00464-018-6191-3A procedure specific assessment tool using 5-point Likert scale, indicating the direction of procedure-specific assessment tools.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hussein AA, Hinata N, Dibaj S, May PR, Kozlowski JD, Abol-Enein H, et al. Development, validation and clinical application of Pelvic Lymphadenectomy Assessment and Completion Evaluation: intraoperative assessment of lymph node dissection after robot-assisted radical cystectomy for bladder cancer. BJU Int. 2017;119:879–84. https://doi.org/10.1111/bju.13748.

    Article  PubMed  Google Scholar 

  13. IJgosse WM, Leijte E, Ganni S, Luursema J-M, Francis NK, Jakimowicz JJ, et al. Competency assessment tool for laparoscopic suturing: development and reliability evaluation. Surg Endosc. 2019;34:2947–53. https://doi.org/10.1007/s00464-019-07077-2.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Goldenberg M, Sadat H, Lee J, Finelli A, Singal R, Grantcharov T. Quantifying the “assistant effect” in robotic-assisted radical prostatectomy: measures of technical performance. J Urol. 2018;199. https://doi.org/10.1016/j.juro.2018.02.124.

  15. Kowalewski TM, Comstock B, Sweet R, Schaffhausen C, Menhadji A, Averch T, et al. Crowd-sourced assessment of technical skills for validation of basic laparoscopic urologic skills tasks. J Urol. 2016;195:1859–65. https://doi.org/10.1016/j.juro.2016.01.005.

    Article  PubMed  Google Scholar 

  16. Vernez SL, Huynh V, Osann K, Okhunov Z, Landman J, Clayman RV. C-SATS: assessing surgical skills among urology residency applicants. J Endourol. 2017;31:S–95–S–100. https://doi.org/10.1089/end.2016.0569.

    Article  Google Scholar 

  17. Wang C, Han L, Stein G, Day S, Bien-Gund C, Mathews A, et al. Crowdsourcing in health and medical research: a systematic review. Infect Dis Poverty. 2020;9:73. https://doi.org/10.1186/s40249-020-0622-9.

    Article  Google Scholar 

  18. Hung AJ, Oh PJ, Chen J, Ghodoussipour S, Lane C, Jarc A, et al. Experts vs super-experts: differences in automated performance metrics and clinical outcomes for robot-assisted radical prostatectomy. BJU Int. 2019;123:861–8. https://doi.org/10.1111/bju.14599A study using events and kinematic data during surgery to differentiate experts vs super-experts, representing a step towards objective surgical assessment.

    Article  PubMed  Google Scholar 

  19. Hung AJ, Chen J, Jarc A, Hatcher D, Djaladat H, Gill IS. Development and validation of objective performance metrics for robot-assisted radical prostatectomy: a pilot study. J Urol. 2018;199:296–304. https://doi.org/10.1016/j.juro.2017.07.081A study using events and kinematic data during surgery to differentiate experts vs novices, representing a step towards objective surgical assessment.

    Article  PubMed  Google Scholar 

  20. Baghdadi A, Hussein AA, Ahmed Y, Cavuoto LA, Guru KA. A computer vision technique for automated assessment of surgical performance using surgeons' console-feed videos. Int J Comput Assist Radiol Surg. 2019;14:697–707. https://doi.org/10.1007/s11548-018-1881-9A pilot study to automate video-based surgical assessment which previously relies heavily on surgical experts and is time-consuming.

    Article  PubMed  Google Scholar 

  21. Harriman D, Singla R, Nguan C. The resident report card: a tool for operative feedback and evaluation of technical skills. J Surg Res. 2019;239:261–8. https://doi.org/10.1016/j.jss.2019.02.006.

    Article  PubMed  Google Scholar 

  22. Merrill SB, Sohl BS, Thompson RH, Reese AC, Parekh DJ, Lynch JH, et al. The balance between open and robotic training among graduating urology residents—does surgical technique need monitoring? J Urol. 2020;203:996–1002. https://doi.org/10.1097/JU.0000000000000689.

    Article  PubMed  Google Scholar 

  23. Kozan AA, Chan LH, Biyani CS. Current status of simulation training in urology: a non-systematic review. Res Rep Urol. 2020;12:111–28. https://doi.org/10.2147/RRU.S237808A comprehensive review about current urologic simulation models.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Rowley K, Pruthi D, Al-Bayati O, Basler J, Liss MA. Novel use of household items in open and robotic surgical skills resident education. Adv Urol. 2019;2019:1–6. https://doi.org/10.1155/2019/5794957A study using common items to make surgical simulation models, increasing the accessibility among trainees.

    Article  Google Scholar 

  25. Dai JC, Ahn JS, Cannon ST, Walsh TJ, Ostrowski K, Raheem OA, et al. Acute ischemic priapism management: an educational and simulation curriculum. MedEdPORTAL. 2018;14:10731. https://doi.org/10.15766/mep_2374-8265.10731.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Pinto LOAD, de Barros CAV, de Lima AB, Santos DRD, de Bacelar HPH. Portable model for vasectomy reversal training. Int Braz J Urol. 2019;45:1013–9. https://doi.org/10.1590/s1677-5538.ibju.2019.0092.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Tatar I, Selcuk I, Huri E. Evaluation of a 3D printed female anatomical model for the hands on training of trans-obturator tape (TOT) and tension free vaginal tape (TVT) sling procedures. Int J Morphol. 2020;38:292–8. https://doi.org/10.4067/S0717-95022020000200292.

    Article  Google Scholar 

  28. van Renterghem K, Ghazi A. 3D pelvic cadaver model: a novel approach to surgical training for penile implant surgery. Int J Impot Res. 2019;32:261–3. https://doi.org/10.1038/s41443-019-0211-2.

    Article  PubMed  Google Scholar 

  29. Nonde J, Laher AE, McDowall J, Adam A. A systematic review of the world of validated suprapubic catheter insertion simulation trainers: from ‘head-blocks‘ to ‘lunch boxes’. Curr Urol. 2020;13:179–88. https://doi.org/10.1159/000499273.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Gao W, Ou T, Jia J, Fan J, Xu J, Li J, et al. Development and evaluation of a training model for paracentetic suprapubic cystostomy and catheterization. Clinics. 2019;74:2153. https://doi.org/10.6061/clinics/2019/e435.

    Article  Google Scholar 

  31. Ahmed K, Aydin A, Dasgupta P, Khan MS, McCabe JE. A novel cadaveric simulation program in urology. J Surg Educ. 2015;72:556–65. https://doi.org/10.1016/j.jsurg.2015.01.005.

    Article  PubMed  Google Scholar 

  32. Coloma L, Cabello R, González C, Quicios C, Bueno G, García JV, et al. Cadaveric models for renal transplant surgery education: a comprehensive review. Curr Urol Rep. 2020;21:2093. https://doi.org/10.1007/s11934-020-0961-x.

    Article  Google Scholar 

  33. Lentz AC, Rodriguez D, Davis LG, Apoj M, Kerfoot BP, Perito P, et al. Simulation training in penile implant surgery: assessment of surgical confidence and knowledge with cadaveric laboratory training. Sex Med. 2018;6:332–8. https://doi.org/10.1016/j.esxm.2018.09.001.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Rojas-Muñoz E, Cabrera ME, Andersen D, Popescu V, Marley S, Mullis B, et al. Surgical telementoring without encumbrance: a comparative study of see-through augmented reality-based approaches. Ann Surg. 2019;270:384–9. https://doi.org/10.1097/SLA.0000000000002764A study using see-through augmented reality technique to mentor surgery, indicating a novel direction of future open surgery simulation.

    Article  PubMed  Google Scholar 

  35. Aditya I, Kwong JCC, Canil T, Lee JY, Goldenberg MG. Current educational interventions for improving technical skills of urology trainees in endourological procedures: a systematic review. J Endourol 2020; [published online ahead of print, 2020 Mar 23]. https://doi.org/10.1089/end.2019.0693.

  36. Janabi Al HF, Aydin A, Palaneer S, Macchione N, Al-Jabir A, Khan MS, et al. Effectiveness of the HoloLens mixed-reality headset in minimally invasive surgery: a simulation-based feasibility study. Surg Endosc. 2019;34:1143–9. https://doi.org/10.1007/s00464-019-06862-3.

    Article  Google Scholar 

  37. Al-Jabir A, Aydın A, Ahmed K, McCabe JE, Khan MS, Dasgupta P, et al. The role of dry-lab and cadaveric simulation for cystoscopy and intravesical Botulinum toxin injections. Transl Androl Urol. 2019;8:673–7. https://doi.org/10.21037/tau.2019.11.11.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Farhan B, Soltani T, Do R, Perez C, Choi H, Ghoniem G. Face, content, and construct validations of endoscopic needle injection simulator for transurethral bulking agent in treatment of stress urinary incontinence. J Surg Educ. 2018;75:1673–8. https://doi.org/10.1016/j.jsurg.2018.04.011.

    Article  PubMed  Google Scholar 

  39. Antunes AA, Iscaife A, Barbosa JABA, Anjos Dos G, Nahas WC, Srougi M. Holmium laser enucleation of the prostate simulation: analysis of realism and level of difficulty by holmium laser enucleation of the prostate-naïve urologists. Urology. 2019;125:34–9. https://doi.org/10.1016/j.urology.2018.10.055.

    Article  PubMed  Google Scholar 

  40. Kailavasan M, Berridge C, Athanasiadis G, Gkentzis A, Rai B, Jain S, et al. Design, implementation, and evaluation of a novel curriculum to teach transurethral resection of the prostate (TURP): a 3-year experience of urology simulation bootcamp course. World J Urol. 2020;100:326–2906. https://doi.org/10.1007/s00345-020-03104-3.

    Article  Google Scholar 

  41. Choi E, Adams F, Palagi S, Gengenbacher A, Schlager D, Müller P-F, et al. A high-fidelity phantom for the simulation and quantitative evaluation of transurethral resection of the prostate. Ann Biomed Eng. 2019;48:437–46. https://doi.org/10.1007/s10439-019-02361-7.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Bube SH, Hansen RB, Dahl C, Konge L, Azawi N. Development and validation of a simulator-based test in transurethral resection of bladder tumours (TURBEST). Scand J Urol. 2019;53:319–24. https://doi.org/10.1080/21681805.2019.1663921.

    Article  CAS  PubMed  Google Scholar 

  43. Neumann E, Mayer J, Russo GI, Amend B, Rausch S, Deininger S, et al. Transurethral resection of bladder tumors: next-generation virtual reality training for surgeons. Eur Urol Focus. 2019;5:906–11. https://doi.org/10.1016/j.euf.2018.04.011.

    Article  PubMed  Google Scholar 

  44. Fiard G, Selmi S-Y, Maigron M, Bellier A, Promayon E, Descotes J-L, et al. Validating the transfer of skills acquired on a prostate biopsy simulator: a prospective, randomized, controlled study. J Surg Educ. 2020;77:953–60. https://doi.org/10.1016/j.jsurg.2020.01.008.

    Article  PubMed  Google Scholar 

  45. de Vries AH, Muijtjens AMM, van Genugten HGJ, Hendrikx AJM, Koldewijn EL, Schout BMA, et al. Development and validation of the TOCO–TURBT tool: a summative assessment tool that measures surgical competency in transurethral resection of bladder tumour. Surg Endosc. 2018;32:4923–31. https://doi.org/10.1007/s00464-018-6251-8.

    Article  PubMed  Google Scholar 

  46. Karagozlu Akgul A, Unal D, Demirbas M, Oner S, Ucar M, Akgul K, et al. A simple, non - biological model for percutaneous renal access training. Urol J. 2018;15:1–5. https://doi.org/10.22037/uj.v0i0.3805.

    Article  PubMed  Google Scholar 

  47. Ewald JM, Cheng JW-C, Engelhart SM, Wilkinson MC, Hajiha M, Wagner H, et al. A realistic, durable, and low-cost training model for percutaneous renal access using ballistic gelatin. Turk J Urol. 2019;45:31–6. https://doi.org/10.5152/tud.2018.43569.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Noureldin YA, Hoenig DM, Zhao P, Elsamra SE, Stern J, Gaunay G, et al. Incorporation of the fluoroless C-arm trainer at the American Urological Association hands on training percutaneous renal access. World J Urol. 2018;36:1149–55. https://doi.org/10.1007/s00345-018-2219-5.

    Article  PubMed  Google Scholar 

  49. Forbes CM, Lim J, Chan J, Paterson RF, Gupta M, Chew BH, et al. Introduction of an ex-vivo pig model for teaching percutaneous nephrolithotomy access techniques. Can Urol Assoc J. 2019;13:355–60. https://doi.org/10.5489/cuaj.5717.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Abboudi H, Khan MS, Guru KA, Froghi S, de Win G, Van Poppel H, et al. Learning curves for urological procedures: a systematic review. BJU Int. 2014;114:617–29. https://doi.org/10.1111/bju.12315.

    Article  PubMed  Google Scholar 

  51. de Oliveira TR, Cleynenbreugel BV, Pereira S, Oliveira P, Gaspar S, Domingues N, et al. Laparoscopic training in urology residency programs: a systematic review. Curr Urol. 2019;12:121–6. https://doi.org/10.1159/000489437.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Fernández-Tomé B, Díaz-Güemes I, Enciso Sanz S, Naranjo Moreno B, Correa L, Sánchez-Hurtado MA, et al. Validation of a new artificial model for simulated training of a laparoscopic vesicourethral anastomosis. Actas Urol Esp. 2019;43:348–54. https://doi.org/10.1016/j.acuroe.2019.07.001.

    Article  PubMed  Google Scholar 

  53. Kailavasan M, Berridge C, Kandaswamy G, Rai B, Wilkinson B, Jain S, et al. A low-cost synthetic abdominal wall model (“Raj Model”) for the training of laparoscopic port insertion. World J Surg. 2020;44:1431–5. https://doi.org/10.1007/s00268-019-05354-8.

    Article  PubMed  Google Scholar 

  54. Travassos TDC, Schneider-Monteiro ED, Santos AMD, Reis LO. Homemade laparoscopic simulator. Acta Cir Bras. 2019;34:e201901006. https://doi.org/10.1590/s0102-865020190100000006.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Parkhomenko E, Yoon R, Okhunov Z, Patel RM, Dolan B, Kaler K, et al. Multi-institutional evaluation of producing and testing a novel 3D-printed laparoscopic trainer. Urology. 2019;124:297–301. https://doi.org/10.1016/j.urology.2018.06.034.

    Article  PubMed  Google Scholar 

  56. Oussi N, Enochsson L, Henningsohn L, Castegren M, Georgiou E, Kjellin A. Trainee performance after laparoscopic simulator training using a Blackbox versus LapMentor. J Surg Res. 2020;250:1–11. https://doi.org/10.1016/j.jss.2019.12.039.

    Article  PubMed  Google Scholar 

  57. Thinggaard E, Bjerrum F, Strandbygaard J, Konge L, Gögenur I. A randomised clinical trial of take-home laparoscopic training. Dan Med J. 2019;66:A5525. https://ugeskriftet.dk/dmj/randomised-clinical-trial-take-home-laparoscopictraining.

  58. Montanari E, Schwameis R, Veit-Rubin N, Kuessel L, Husslein H. Basic laparoscopic skills training is equally effective using 2D compared to 3D visualization: a randomized controlled trial. Jcm. 2020;9:1408. https://doi.org/10.3390/jcm9051408.

    Article  Google Scholar 

  59. Clements MB, Morrison KY, Schenkman NS. Evaluation of laparoscopic curricula in American Urology Residency Training: a 5-year update. J Endourol. 2016;30:347–53. https://doi.org/10.1089/end.2015.0561.

    Article  PubMed  Google Scholar 

  60. Intuitive Surgical. 2019 Annual Report. 2020:1–121. https://www.annualreports.com/HostedData/AnnualReports/PDF/NASDAQ_ISRG_2019.pdf. Accessed 6 June 2020.

  61. Ghazi AE, Teplitz BA. Role of 3D printing in surgical education for robotic urology procedures. Transl Androl Urol. 2020;9:931–41. https://doi.org/10.21037/tau.2020.01.03.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Chen MY, Skewes J, Desselle M, Wong C, Woodruff MA, Dasgupta P, et al. Current applications of three-dimensional printing in urology. BJU Int. 2019;125:17–27. https://doi.org/10.1111/bju.14928.

    Article  PubMed  Google Scholar 

  63. Melnyk R, Ezzat B, Belfast E, Saba P, Farooq S, Campbell T, et al. Mechanical and functional validation of a perfused, robot-assisted partial nephrectomy simulation platform using a combination of 3D printing and hydrogel casting. World J Urol. 2019;38:1631–41. https://doi.org/10.1007/s00345-019-02989-zA high-fidelity kidney model with perfused renal hilum was produced by 3D printing combined with hydrogel casting.

    Article  CAS  PubMed  Google Scholar 

  64. Witthaus MW, Farooq S, Melnyk R, Campbell T, Saba P, Mathews E, et al. Incorporation and validation of clinically relevant performance metrics of simulation (CRPMS) into a novel full-immersion simulation platform for nerve-sparing robot-assisted radical prostatectomy (NS-RARP) utilizing three-dimensional printing and hydroge. BJU Int. 2019;125:322–32. https://doi.org/10.1111/bju.14940A high-fidelity prostate model can provide full-immersion simulation for robotic-assisted radical prostatectomy.

    Article  PubMed  Google Scholar 

  65. Moglia A, Ferrari V, Morelli L, Ferrari M, Mosca F, Cuschieri A. A systematic review of virtual reality simulators for robot-assisted surgery. Eur Urol. 2016;69:1065–80. https://doi.org/10.1016/j.eururo.2015.09.021.

    Article  PubMed  Google Scholar 

  66. Tan X, Chng C, Su Y, Lim K, Chui C. Robot-assisted training in laparoscopy using deep reinforcement learning. IEEE Robot Autom Lett. 2019;4:485–92.

    Article  Google Scholar 

  67. Pandya A, Eslamian S, Ying H, Nokleby M, Reisner LA. A robotic recording and playback platform for training surgeons and learning autonomous behaviors using the da Vinci surgical system. Robotics. 2019;8:9. https://doi.org/10.3390/robotics8010009A novel training system which can replay video with synchronized expert hand-motion.

    Article  Google Scholar 

  68. Cimen HI, Atik YT, Altinova S, Adsan O, Balbay MD. Does the experience of the bedside assistant effect the results of robotic surgeons in the learning curve of robot assisted radical prostatectomy? Int Braz J Urol. 2019;45:54–60. https://doi.org/10.1590/s1677-5538.ibju.2018.0184.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Cimen HI, Atik YT, Gul D, Uysal B, Balbay MD. Serving as a bedside surgeon before performing robotic radical prostatectomy improves surgical outcomes. Int Braz J Urol. 2019;45:1122–8. https://doi.org/10.1590/s1677-5538.ibju.2019.0330.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Collins JW, Levy J, Stefanidis D, Gallagher A, Coleman M, Cecil T, et al. Utilising the Delphi process to develop a proficiency-based progression train-the-trainer course for robotic surgery training. Eur Urol. 2019;75:775–85. https://doi.org/10.1016/j.eururo.2018.12.044A pilot study about train-the-trainer course, highlighting the importance of trainer’s ability in surgical training.

    Article  PubMed  Google Scholar 

  71. Navaratnam A, Abdul-Muhsin H, Humphreys M. Updates in urologic robot assisted surgery. F1000Res. 2018;7:1948. https://doi.org/10.12688/f1000research.15480.1.

    Article  Google Scholar 

  72. Collins JW, Dell'Oglio P, Hung AJ, Brook NR. The importance of technical and non-technical skills in robotic surgery training. Eur Urol Focus. 2018;4:674–6. https://doi.org/10.1016/j.euf.2018.08.018.

    Article  PubMed  Google Scholar 

  73. Brunckhorst O, Khan MS, Dasgupta P, Ahmed K. Effective non-technical skills are imperative to robot-assisted surgery. BJU Int. 2015;116:842–4. https://doi.org/10.1111/bju.12934.

    Article  PubMed  Google Scholar 

  74. Anderson O, Davis R, Hanna GB, Vincent CA. Surgical adverse events: a systematic review. Am J Surg. 2013;206:253–62. https://doi.org/10.1016/j.amjsurg.2012.11.009.

    Article  PubMed  Google Scholar 

  75. Leuschner S, Leuschner M, Kropf S, Niederbichler AD. Non-technical skills training in the operating theatre: a meta-analysis of patient outcomes. Surgeon. 2019;17:233–43. https://doi.org/10.1016/j.surge.2018.07.001.

    Article  PubMed  Google Scholar 

  76. Griffin C, Aydın A, Brunckhorst O, Raison N, Khan MS, Dasgupta P, et al. Non-technical skills: a review of training and evaluation in urology. World J Urol. 2019;38:1653–61. https://doi.org/10.1007/s00345-019-02920-6.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Wood TC, Raison N, Haldar S, Brunckhorst O, McIlhenny C, Dasgupta P, et al. Training tools for nontechnical skills for surgeons—a systematic review. J Surg Educ. 2017;74:548–78. https://doi.org/10.1016/j.jsurg.2016.11.017.

    Article  PubMed  Google Scholar 

  78. Raison N, Wood T, Brunckhorst O, Abe T, Ross T, Challacombe B, et al. Development and validation of a tool for non-technical skills evaluation in robotic surgery—the ICARS system. Surg Endosc. 2017;31:5403–10. https://doi.org/10.1007/s00464-017-5622-x.

    Article  PubMed  Google Scholar 

  79. Ounounou E, Aydin A, Brunckhorst O, Khan MS, Dasgupta P, Ahmed K. Nontechnical skills in surgery: a systematic review of current training modalities. J Surg Educ. 2019;76:14–24. https://doi.org/10.1016/j.jsurg.2018.05.017.

    Article  PubMed  Google Scholar 

  80. Somasundram K, Spence H, Colquhoun AJ, McIlhenny C, Biyani CS, Jain S. Simulation in urology to train non-technical skills in ward rounds. BJU Int. 2018;122:705–12. https://doi.org/10.1111/bju.14402.

    Article  PubMed  Google Scholar 

  81. Goldenberg MG, Fok KH, Ordon M, Pace KT, Lee JY. Simulation-based laparoscopic surgery crisis resource management training—predicting technical and nontechnical skills. J Surg Educ. 2018;75:1113–9. https://doi.org/10.1016/j.jsurg.2017.11.011.

    Article  PubMed  Google Scholar 

  82. Nelson B, Na Eun K, Sullivan B, O'Neal P, Sanchez V, Whang E, et al. Playing the surgical technologist role by surgery residents improves their technical and nontechnical skills. J Surg Res. 2019;238:57–63. https://doi.org/10.1016/j.jss.2019.01.026.

    Article  PubMed  Google Scholar 

  83. Liao C-H, Ooyang C-H, Chen C-C, Liao C-A, Cheng C-T, Hsieh M-J, et al. Video coaching improving contemporary technical and nontechnical ability in laparoscopic education. J Surg Educ. 2020;77:652–60. https://doi.org/10.1016/j.jsurg.2019.11.012.

    Article  PubMed  Google Scholar 

  84. Prebay ZJ, Peabody JO, Miller DC, Ghani KR. Video review for measuring and improving skill in urological surgery. Nat Rev Urol. 2019;16:261–7. https://doi.org/10.1038/s41585-018-0138-2A comprehensive review of video-based surgical assessment and coaching.

    Article  PubMed  Google Scholar 

  85. Greenberg CC, Dombrowski J, Dimick JB. Video-based surgical coaching. JAMA Surg. 2016;151:282. https://doi.org/10.1001/jamasurg.2015.4442.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Cathcart P, Sridhara A, Ramachandran N, Briggs T, Nathan S, Kelly J. Achieving quality assurance of prostate cancer surgery during reorganisation of cancer services. Eur Urol. 2015;68:22–9. https://doi.org/10.1016/j.eururo.2015.02.028.

    Article  PubMed  Google Scholar 

  87. Sarle R, Rakic N, Kim T, Brachulis A, Lane BR, Stockton B, et al. Surgical skill quality improvement: utilizing a peer video review workshop for surgeons performing robotic prostatectomy. J Urol. 2017;197:E698. https://doi.org/10.1016/j.juro.2017.02.1622.

    Article  Google Scholar 

  88. Kun Y, Hubert J, Bin L, Huan WX. Self-debriefing model based on an integrated video-capture system: an efficient solution to skill degradation. J Surg Educ. 2019;76:362–9. https://doi.org/10.1016/j.jsurg.2018.08.017.

    Article  PubMed  Google Scholar 

  89. Celentano V, Smart N, Cahill RA, McGrath JS, Gupta S, Griffith JP, et al. Use of laparoscopic videos amongst surgical trainees in the United Kingdom. Surgeon. 2019;17:334–9. https://doi.org/10.1016/j.surge.2018.10.004.

    Article  PubMed  Google Scholar 

  90. Arslan B, Gönültaş S, Gökmen E, Özman O, Onuk Ö, Yazıcı G, et al. Does YouTube include high-quality resources for training on laparoscopic and robotic radical prostatectomy? World J Urol. 2019;38:1195–9. https://doi.org/10.1007/s00345-019-02904-6.

    Article  PubMed  Google Scholar 

  91. Baber J, Staff I, McLaughlin T, Tortora J, Champagne A, Gangakhedkar A, et al. Impact of urology resident Involvement on intraoperative, long-term oncologic and functional outcomes of robotic assisted laparoscopic radical prostatectomy. Urology. 2019;132:43–8. https://doi.org/10.1016/j.urology.2019.05.040.

    Article  PubMed  Google Scholar 

  92. Privé B, Kortleve M, van Basten J-P. Evaluating the impact of resident involvement during the laparoscopic nephrectomy. Cent Eur J Urol. 2019;72:369–73. https://doi.org/10.5173/ceju.2019.0021.

    Article  Google Scholar 

  93. Chen A, Ghodoussipour S, Titus MB, Nguyen JH, Chen J, Ma R, et al. Comparison of clinical outcomes and automated performance metrics in robot-assisted radical prostatectomy with and without trainee involvement. World J Urol. 2019;38:1615–21. https://doi.org/10.1007/s00345-019-03010-3.

    Article  PubMed  Google Scholar 

  94. Holland BC, Patel N, Sulaver R, Stevenson B, Healey J, Severino W, et al. Resident impact on patient & surgeon satisfaction and outcomes: evidence for health system support for urology education. Urology. 2019;132:49–55. https://doi.org/10.1016/j.urology.2019.04.043.

    Article  PubMed  Google Scholar 

  95. Aisen CM, James M, Chung DE. The impact of teaching on fundamental general urologic procedures: do residents help or hurt? Urology. 2018;121:44–50. https://doi.org/10.1016/j.urology.2018.05.044.

    Article  PubMed  Google Scholar 

  96. Kim SSY, Blankstein U, Ordon M, Pace KT, Honey RJD, Lee JY, et al. Evaluation of optimal timing of expert feedback in a simulated flexible ureteroscopy course. J Endourol. 2019;33:463–7. https://doi.org/10.1089/end.2018.0732.

    Article  PubMed  Google Scholar 

  97. Carrion DM, Gómez Rivas J, Esperto F, Patruno G, Vásquez JL. Current status of urological training in Europe. Arch Esp Urol. 2018;71:11–7.

    PubMed  Google Scholar 

  98. Okhunov Z, Safiullah S, Patel R, Juncal S, Garland H, Khajeh NR, et al. evaluation of urology residency training and perceived resident abilities in the United States. J Surg Educ. 2019;76:936–48. https://doi.org/10.1016/j.jsurg.2019.02.002.

    Article  PubMed  Google Scholar 

  99. Almarzouq A, Hu J, Noureldin YA, Yin A, Anidjar M, Bladou F, et al. Are basic robotic surgical skills transferable from the simulator to the operating room? A randomized, prospective, educational study. Can Urol Assoc J. 2020;14:416–22. https://doi.org/10.5489/cuaj.6460.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Aydin A, Ahmed K, Van Hemelrijck M, Ahmed HU, Khan MS, Dasgupta P, et al. Simulation in Urological Training and Education (SIMULATE): protocol and curriculum development of the first multicentre international randomized controlled trial assessing the transferability of simulation-based surgical training. BJU Int. 2020;13:503. https://doi.org/10.1111/bju.15056An international randomized controlled clinical trial which inspects whether a structured ureteroscopy (URS) curriculum reduces complication rates of the first 25 URS cases of novices, representing one of the initial international collaborations on this topic.

    Article  Google Scholar 

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Correspondence to Andrew J. Hung.

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Andrew J. Hung has financial disclosures with Quantgene, Inc. (consultant), Mimic Technologies, Inc. (consultant), and Johnson and Johnson, Inc. (consultant)

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Ma, R., Reddy, S., Vanstrum, E.B. et al. Innovations in Urologic Surgical Training. Curr Urol Rep 22, 26 (2021). https://doi.org/10.1007/s11934-021-01043-z

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