Literature DB >> 19915915

Objective classification of residents based on their psychomotor laparoscopic skills.

Magdalena K Chmarra1, Stefan Klein, Joost C F de Winter, Frank-Willem Jansen, Jenny Dankelman.   

Abstract

BACKGROUND: From the clinical point of view, it is important to recognize residents' level of expertise with regard to basic psychomotor skills. For that reason, surgeons and surgical organizations (e.g., Acreditation Council for Graduate Medical Education, ACGME) are calling for assessment tools that credential residents as technically competent. Currently, no method is universally accepted or recommended for classifying residents as "experienced," "intermediates," or "novices" according to their technical abilities. This study introduces a classification method for recognizing residents' level of experience in laparoscopic surgery based on psychomotor laparoscopic skills alone.
METHODS: For this study, 10 experienced residents (>100 laparoscopic procedures performed), 10 intermediates (10-100 procedures performed), and 11 novices (no experience) performed four tasks in a box trainer. The movements of the laparoscopic instruments were recorded with the TrEndo tracking system and analyzed using six motion analysis parameters (MAPs). The MAPs of all participants were submitted to principal component analysis (PCA), a data reduction technique. The scores of the first principal components were used to perform linear discriminant analysis (LDA), a classification method. Performance of the LDA was examined using a leave-one-out cross-validation.
RESULTS: Of 31 participants, 23 were classified correctly with the proposed method, with 7 categorized as experienced, 7 as intermediates, and 9 as novices.
CONCLUSIONS: The proposed method provides a means to classify residents objectively as experienced, intermediate, or novice surgeons according to their basic laparoscopic skills. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.

Entities:  

Mesh:

Year:  2009        PMID: 19915915      PMCID: PMC2860557          DOI: 10.1007/s00464-009-0721-y

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


  23 in total

1.  Assessing laparoscopic manipulative skills.

Authors:  C D Smith; T M Farrell; S S McNatt; R E Metreveli
Journal:  Am J Surg       Date:  2001-06       Impact factor: 2.565

2.  The challenge of objective assessment of surgical skill.

Authors:  A Darzi; V Datta; S Mackay
Journal:  Am J Surg       Date:  2001-06       Impact factor: 2.565

3.  Bimodal assessment of laparoscopic suturing skills: construct and concurrent validity.

Authors:  K Moorthy; Y Munz; A Dosis; F Bello; A Chang; A Darzi
Journal:  Surg Endosc       Date:  2004-10-11       Impact factor: 4.584

4.  The MISTELS program to measure technical skill in laparoscopic surgery : evidence for reliability.

Authors:  M C Vassiliou; G A Ghitulescu; L S Feldman; D Stanbridge; K Leffondré; H H Sigman; G M Fried
Journal:  Surg Endosc       Date:  2006-02-27       Impact factor: 4.584

5.  Motion analysis in the training and assessment of minimally invasive surgery.

Authors: 
Journal:  Minim Invasive Ther Allied Technol       Date:  2003-07       Impact factor: 2.442

6.  Simulators in surgery.

Authors:  Fredrik H Halvorsen; Ole Jakob Elle; Erik Fosse
Journal:  Minim Invasive Ther Allied Technol       Date:  2005       Impact factor: 2.442

7.  An evaluation of the feasibility, validity, and reliability of laparoscopic skills assessment in the operating room.

Authors:  Rajesh Aggarwal; Teodor Grantcharov; Krishna Moorthy; Thor Milland; Pavlos Papasavas; Aristotelis Dosis; Fernando Bello; Ara Darzi
Journal:  Ann Surg       Date:  2007-06       Impact factor: 12.969

Review 8.  Systems for tracking minimally invasive surgical instruments.

Authors:  M K Chmarra; C A Grimbergen; J Dankelman
Journal:  Minim Invasive Ther Allied Technol       Date:  2007       Impact factor: 2.442

Review 9.  Evolution of surgical skills training.

Authors:  Kurt-E Roberts; Robert-L Bell; Andrew-J Duffy
Journal:  World J Gastroenterol       Date:  2006-05-28       Impact factor: 5.742

10.  Multivariate examination of brain abnormality using both structural and functional MRI.

Authors:  Yong Fan; Hengyi Rao; Hallam Hurt; Joan Giannetta; Marc Korczykowski; David Shera; Brian B Avants; James C Gee; Jiongjiong Wang; Dinggang Shen
Journal:  Neuroimage       Date:  2007-04-19       Impact factor: 6.556

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  28 in total

1.  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

2.  Subjective vs Computerized Assessment of Surgeon Skill Level During Mastoidectomy.

Authors:  Michaela F Close; Charmee H Mehta; Yuan Liu; Mitchell J Isaac; Mark S Costello; Kyle D Kulbarsh; Ted A Meyer
Journal:  Otolaryngol Head Neck Surg       Date:  2020-06-30       Impact factor: 3.497

3.  Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis.

Authors:  Ignacio Oropesa; Patricia Sánchez-Gonzáez; Magdalena K Chmarra; Pablo Lamata; Rodrigo Pérez-Rodríguez; Frank Willem Jansen; Jenny Dankelman; Enrique J Gómez
Journal:  Surg Endosc       Date:  2014-02       Impact factor: 4.584

4.  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

5.  The minimally acceptable classification criterion for surgical skill: intent vectors and separability of raw motion data.

Authors:  Rodney L Dockter; Thomas S Lendvay; Robert M Sweet; Timothy M Kowalewski
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-05-18       Impact factor: 2.924

6.  Computerized model for objectively evaluating cutting performance using a laparoscopic box trainer simulator.

Authors:  Amir Handelman; Shani Schnaider; Adva Schwartz-Ossad; Refael Barkan; Ronnie Tepper
Journal:  Surg Endosc       Date:  2018-11-26       Impact factor: 4.584

Review 7.  Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery.

Authors:  Ziheng Wang; Ann Majewicz Fey
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-09-25       Impact factor: 2.924

Review 8.  Is motion analysis a valid tool for assessing laparoscopic skill?

Authors:  John D Mason; James Ansell; Neil Warren; Jared Torkington
Journal:  Surg Endosc       Date:  2012-12-12       Impact factor: 4.584

9.  Learning curve on the TrEndo laparoscopic simulator compared to an expert level.

Authors:  Pieter J van Empel; Joris P Commandeur; Lennart B van Rijssen; Mathilde G E Verdam; Judith A Huirne; Fedde Scheele; H Jaap Bonjer; W Jeroen Meijerink
Journal:  Surg Endosc       Date:  2013-02-23       Impact factor: 4.584

10.  Learning from visual force feedback in box trainers: tissue manipulation in laparoscopic surgery.

Authors:  Tim Horeman; Freek van Delft; Mathijs D Blikkendaal; Jenny Dankelman; John J van den Dobbelsteen; Frank-Willem Jansen
Journal:  Surg Endosc       Date:  2014-02-12       Impact factor: 4.584

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