Literature DB >> 24872773

Robotic microsurgical training and evaluation.

Jesse C Selber1, Taiba Alrasheed1.   

Abstract

Robotic surgery has expanded rapidly over the past two decades and is in widespread use among the surgical subspecialties. Clinical applications in plastic surgery have emerged gradually over the last few years. One of the promising applications is robotic-assisted microvascular anastomosis. Here the authors first describe a process by which an assessment instrument they developed called the Structured Assessment of Robotic Microsurgical Skills (SARMS) was validated. The instrument combines the previously validated Structured Assessment of Microsurgical Skills (SAMS) with other skill domains in robotic surgery. Interrater reliability for the SARMS instrument was excellent for all skill areas among four expert, blinded evaluators. They then present a process by which the learning curve for robotic-assisted microvascular anastomoses was measured and plotted. Ten study participants performed five robotic microanastomoses each that were recorded, deidentified and scored. Trends in SARMS scores were plotted. All skill areas and overall performance improved significantly for each participant over the five microanastomotic sessions, and operative time decreased for all participants. The results showed an initial steep ascent in technical skill acquisition followed by more gradual improvement, and a steady decrease in operative times for the cohort. Participants at all levels of training, ranging from minimal microsurgical experience to expert microsurgeons gained proficiency over the course of five robotic sessions.

Entities:  

Keywords:  robotic microsurgery; robotic skills assessment; surgical evaluation; surgical skills assessment; surgical training

Year:  2014        PMID: 24872773      PMCID: PMC3946019          DOI: 10.1055/s-0034-1368161

Source DB:  PubMed          Journal:  Semin Plast Surg        ISSN: 1535-2188            Impact factor:   2.314


  12 in total

1.  Assessing operative skill. Needs to become more objective.

Authors:  A Darzi; S Smith; N Taffinder
Journal:  BMJ       Date:  1999-04-03

2.  Qualitative and quantitative analysis of the learning curve of a simulated surgical task on the da Vinci system.

Authors:  J D Hernandez; S D Bann; Y Munz; K Moorthy; V Datta; S Martin; A Dosis; F Bello; A Darzi; T Rockall
Journal:  Surg Endosc       Date:  2004-02-02       Impact factor: 4.584

3.  Learning curve of robotic-assisted microvascular anastomosis in the rat.

Authors:  Joo-Yup Lee; Tiago Mattar; Thomas J Parisi; Brian T Carlsen; Allen T Bishop; Alexander Y Shin
Journal:  J Reconstr Microsurg       Date:  2011-09-29       Impact factor: 2.873

4.  Transfer of training in robotic-assisted microvascular surgery.

Authors:  Raffy L Karamanoukian; Trung Bui; Michael P McConnell; Gregory R D Evans; Hratch L Karamanoukian
Journal:  Ann Plast Surg       Date:  2006-12       Impact factor: 1.539

Review 5.  Validation of microsurgical models in microsurgery training and competence: a review.

Authors:  Woan-Yi Chan; Paolo Matteucci; Stephen J Southern
Journal:  Microsurgery       Date:  2007       Impact factor: 2.425

Review 6.  Development of a training curriculum for microsurgery.

Authors:  Indran Balasundaram; Rajesh Aggarwal; Lord Ara Darzi
Journal:  Br J Oral Maxillofac Surg       Date:  2010-01-06       Impact factor: 1.651

7.  The future of robotics in hand surgery.

Authors:  P Liverneaux; E Nectoux; C Taleb
Journal:  Chir Main       Date:  2009-08-29

8.  Developing a comprehensive, proficiency-based training program for robotic surgery.

Authors:  Genevieve Dulan; Robert V Rege; Deborah C Hogg; Kristine M Gilberg-Fisher; Nabeel A Arain; Seifu T Tesfay; Daniel J Scott
Journal:  Surgery       Date:  2012-09       Impact factor: 3.982

9.  Tracking the learning curve in microsurgical skill acquisition.

Authors:  Jesse C Selber; Edward I Chang; Jun Liu; Hiroo Suami; David M Adelman; Patrick Garvey; Matthew M Hanasono; Charles E Butler
Journal:  Plast Reconstr Surg       Date:  2012-10       Impact factor: 4.730

10.  Robotic hysterectomy and pelvic-aortic lymphadenectomy for endometrial cancer.

Authors:  Leigh G Seamon; David E Cohn; Debra L Richardson; Sue Valmadre; Matthew J Carlson; Gary S Phillips; Jeffrey M Fowler
Journal:  Obstet Gynecol       Date:  2008-12       Impact factor: 7.661

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  4 in total

1.  A Systematic Review of the Role of Robotics in Plastic and Reconstructive Surgery-From Inception to the Future.

Authors:  Thomas D Dobbs; Olivia Cundy; Harsh Samarendra; Khurram Khan; Iain Stuart Whitaker
Journal:  Front Surg       Date:  2017-11-15

Review 2.  Machine learning for technical skill assessment in surgery: a systematic review.

Authors:  Kyle Lam; Junhong Chen; Zeyu Wang; Fahad M Iqbal; Ara Darzi; Benny Lo; Sanjay Purkayastha; James M Kinross
Journal:  NPJ Digit Med       Date:  2022-03-03

3.  Methodology in Conventional Head and Neck Reconstruction Following Robotic Cancer Surgery: A Bridgehead Robotic Head and Neck Reconstruction.

Authors:  Jongmin Won; Jong Won Hong; Mi Jung Kim; In-Sik Yun; Woo Yeol Baek; Won Jai Lee; Dae Hyun Lew; Yoon Woo Koh; Se-Heon Kim
Journal:  Yonsei Med J       Date:  2022-08       Impact factor: 3.052

Review 4.  Robotic-Assisted Microsurgery and Its Future in Plastic Surgery.

Authors:  Matthias M Aitzetmüller; Marie-Luise Klietz; Alexander F Dermietzel; Tobias Hirsch; Maximilian Kückelhaus
Journal:  J Clin Med       Date:  2022-06-13       Impact factor: 4.964

  4 in total

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