Literature DB >> 25584468

A review of training research and virtual reality simulators for the da Vinci surgical system.

May Liu1, Myriam Curet.   

Abstract

UNLABELLED: PHENOMENON: Virtual reality simulators are the subject of several recent studies of skills training for robot-assisted surgery. Yet no consensus exists regarding what a core skill set comprises or how to measure skill performance. Defining a core skill set and relevant metrics would help surgical educators evaluate different simulators. APPROACH: This review draws from published research to propose a core technical skill set for using the da Vinci surgeon console. Publications on three commercial simulators were used to evaluate the simulators' content addressing these skills and associated metrics.
FINDINGS: An analysis of published research suggests that a core technical skill set for operating the surgeon console includes bimanual wristed manipulation, camera control, master clutching to manage hand position, use of third instrument arm, activating energy sources, appropriate depth perception, and awareness of forces applied by instruments. Validity studies of three commercial virtual reality simulators for robot-assisted surgery suggest that all three have comparable content and metrics. However, none have comprehensive content and metrics for all core skills. INSIGHTS: Virtual reality simulation remains a promising tool to support skill training for robot-assisted surgery, yet existing commercial simulator content is inadequate for performing and assessing a comprehensive basic skill set. The results of this evaluation help identify opportunities and challenges that exist for future developments in virtual reality simulation for robot-assisted surgery. Specifically, the inclusion of educational experts in the development cycle alongside clinical and technological experts is recommended.

Keywords:  robotics; skills assessment; surgery; training; virtual reality simulation

Mesh:

Year:  2015        PMID: 25584468     DOI: 10.1080/10401334.2014.979181

Source DB:  PubMed          Journal:  Teach Learn Med        ISSN: 1040-1334            Impact factor:   2.414


  11 in total

Review 1.  Current state of virtual reality simulation in robotic surgery training: a review.

Authors:  Justin D Bric; Derek C Lumbard; Matthew J Frelich; Jon C Gould
Journal:  Surg Endosc       Date:  2015-08-25       Impact factor: 4.584

2.  Novel evaluation of surgical activity recognition models using task-based efficiency metrics.

Authors:  Aneeq Zia; Liheng Guo; Linlin Zhou; Irfan Essa; Anthony Jarc
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-07-02       Impact factor: 2.924

3.  Assessment of Robotic Console Skills (ARCS): construct validity of a novel global rating scale for technical skills in robotically assisted surgery.

Authors:  May Liu; Shreya Purohit; Joshua Mazanetz; Whitney Allen; Usha S Kreaden; Myriam Curet
Journal:  Surg Endosc       Date:  2017-07-01       Impact factor: 4.584

Review 4.  Innovations in surgery simulation: a review of past, current and future techniques.

Authors:  Ido Badash; Karen Burtt; Carlos A Solorzano; Joseph N Carey
Journal:  Ann Transl Med       Date:  2016-12

5.  Robotic skills can be aided by laparoscopic training.

Authors:  Daniel G Davila; Melissa C Helm; Matthew J Frelich; Jon C Gould; Matthew I Goldblatt
Journal:  Surg Endosc       Date:  2017-12-06       Impact factor: 4.584

6.  Face and content validity of Xperience Team Trainer: bed-side assistant training simulator for robotic surgery.

Authors:  Andrea Moglia
Journal:  Updates Surg       Date:  2018-02-14

7.  Face and content validity of Xperience™ Team Trainer: bed-side assistant training simulator for robotic surgery.

Authors:  Luca Sessa; Cyril Perrenot; Song Xu; Jacques Hubert; Laurent Bresler; Laurent Brunaud; Manuela Perez
Journal:  Updates Surg       Date:  2017-12-20

Review 8.  Objective assessment of robotic surgical skills: review of literature and future directions.

Authors:  Saratu Kutana; Daniel P Bitner; Poppy Addison; Paul J Chung; Mark A Talamini; Filippo Filicori
Journal:  Surg Endosc       Date:  2022-02-28       Impact factor: 3.453

9.  Deep neural networks are effective tools for assessing performance during surgical training.

Authors:  Roger Smith; Danielle Julian; Ariel Dubin
Journal:  J Robot Surg       Date:  2021-07-15

Review 10.  Dental Robotics: A Disruptive Technology.

Authors:  Paras Ahmad; Mohammad Khursheed Alam; Ali Aldajani; Abdulmajeed Alahmari; Amal Alanazi; Martin Stoddart; Mohammed G Sghaireen
Journal:  Sensors (Basel)       Date:  2021-05-11       Impact factor: 3.576

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.