Literature DB >> 15216861

Computer-enhanced laparoscopic training system (CELTS): bridging the gap.

N Stylopoulos1, S Cotin, S K Maithel, M Ottensmeye, P G Jackson, R S Bardsley, P F Neumann, D W Rattner, S L Dawson.   

Abstract

BACKGROUND: There is a large and growing gap between the need for better surgical training methodologies and the systems currently available for such training. In an effort to bridge this gap and overcome the disadvantages of the training simulators now in use, we developed the Computer-Enhanced Laparoscopic Training System (CELTS).
METHODS: CELTS is a computer-based system capable of tracking the motion of laparoscopic instruments and providing feedback about performance in real time. CELTS consists of a mechanical interface, a customizable set of tasks, and an Internet-based software interface. The special cognitive and psychomotor skills a laparoscopic surgeon should master were explicitly defined and transformed into quantitative metrics based on kinematics analysis theory. A single global standardized and task-independent scoring system utilizing a z-score statistic was developed. Validation exercises were performed.
RESULTS: The scoring system clearly revealed a gap between experts and trainees, irrespective of the task performed; none of the trainees obtained a score above the threshold that distinguishes the two groups. Moreover, CELTS provided educational feedback by identifying the key factors that contributed to the overall score. Among the defined metrics, depth perception, smoothness of motion, instrument orientation, and the outcome of the task are major indicators of performance and key parameters that distinguish experts from trainees. Time and path length alone, which are the most commonly used metrics in currently available systems, are not considered good indicators of performance.
CONCLUSION: CELTS is a novel and standardized skills trainer that combines the advantages of computer simulation with the features of the traditional and popular training boxes. CELTS can easily be used with a wide array of tasks and ensures comparability across different training conditions. This report further shows that a set of appropriate and clinically relevant performance metrics can be defined and a standardized scoring system can be designed.

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Year:  2004        PMID: 15216861     DOI: 10.1007/s00464-003-8932-0

Source DB:  PubMed          Journal:  Surg Endosc        ISSN: 0930-2794            Impact factor:   4.584


  3 in total

1.  Virtual reality training improves operating room performance: results of a randomized, double-blinded study.

Authors:  Neal E Seymour; Anthony G Gallagher; Sanziana A Roman; Michael K O'Brien; Vipin K Bansal; Dana K Andersen; Richard M Satava
Journal:  Ann Surg       Date:  2002-10       Impact factor: 12.969

2.  Development of a model for training and evaluation of laparoscopic skills.

Authors:  A M Derossis; G M Fried; M Abrahamowicz; H H Sigman; J S Barkun; J L Meakins
Journal:  Am J Surg       Date:  1998-06       Impact factor: 2.565

3.  Skill acquisition and assessment for laparoscopic surgery.

Authors:  J C Rosser; L E Rosser; R S Savalgi
Journal:  Arch Surg       Date:  1997-02
  3 in total
  15 in total

1.  Electronic device for endosurgical skills training (EDEST): study of reliability.

Authors:  J B Pagador; J Uson; M A Sánchez; J L Moyano; J Moreno; P Bustos; J Mateos; F M Sánchez-Margallo
Journal:  Int J Comput Assist Radiol Surg       Date:  2010-08-11       Impact factor: 2.924

2.  Simulated laparoscopy using a head-mounted display vs traditional video monitor: an assessment of performance and muscle fatigue.

Authors:  S K Maithel; L Villegas; N Stylopoulos; S Dawson; D B Jones
Journal:  Surg Endosc       Date:  2004-12-23       Impact factor: 4.584

3.  Tissue consistency perception in laparoscopy to define the level of fidelity in virtual reality simulation.

Authors:  P Lamata; E J Gómez; F M Sánchez-Margallo; F Lamata; F del Pozo; J Usón
Journal:  Surg Endosc       Date:  2006-07-20       Impact factor: 4.584

4.  Automatic supervision of gestures to guide novice surgeons during training.

Authors:  C Monserrat; A Lucas; J Hernández-Orallo; M José Rupérez
Journal:  Surg Endosc       Date:  2013-11-07       Impact factor: 4.584

5.  Support vector machines improve the accuracy of evaluation for the performance of laparoscopic training tasks.

Authors:  Brian Allen; Vasile Nistor; Erik Dutson; Greg Carman; Catherine Lewis; Petros Faloutsos
Journal:  Surg Endosc       Date:  2009-06-16       Impact factor: 4.584

6.  Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions.

Authors:  Matthew S Holden; Sean Xia; Hillary Lia; Zsuzsanna Keri; Colin Bell; Lindsey Patterson; Tamas Ungi; Gabor Fichtinger
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-04-20       Impact factor: 2.924

7.  The Heidelberg VR Score: development and validation of a composite score for laparoscopic virtual reality training.

Authors:  Mona W Schmidt; Karl-Friedrich Kowalewski; Marc L Schmidt; Erica Wennberg; Carly R Garrow; Sang Paik; Laura Benner; Marlies P Schijven; Beat P Müller-Stich; Felix Nickel
Journal:  Surg Endosc       Date:  2018-10-16       Impact factor: 4.584

8.  A computer vision technique for automated assessment of surgical performance using surgeons' console-feed videos.

Authors:  Amir Baghdadi; Ahmed A Hussein; Youssef Ahmed; Lora A Cavuoto; Khurshid A Guru
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-11-20       Impact factor: 2.924

9.  Construct validity of a video-tracking system based on orthogonal cameras approach for objective assessment of laparoscopic skills.

Authors:  Fernando Pérez-Escamirosa; Alberto Chousleb-Kalach; Maria Del Carmen Hernández-Baro; Juan Alberto Sánchez-Margallo; Daniel Lorias-Espinoza; Arturo Minor-Martínez
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-04-02       Impact factor: 2.924

Review 10.  What is going on in augmented reality simulation in laparoscopic surgery?

Authors:  Sanne M B I Botden; Jack J Jakimowicz
Journal:  Surg Endosc       Date:  2008-09-24       Impact factor: 4.584

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