Literature DB >> 28541193

Analysis of Energy-Based Metrics for Laparoscopic Skills Assessment.

Behnaz Poursartip, Marie-Eve LeBel, Rajni V Patel, Michael D Naish, Ana Luisa Trejos.   

Abstract

OBJECTIVE: The complexity of minimally invasive surgery (MIS) requires that trainees practice MIS skills in numerous training sessions. The goal of these training sessions is to learn how to move the instruments smoothly without damaging the surrounding tissue and achieving operative tasks with accuracy. In order to enhance the efficiency of these training sessions, the proficiency of the trainees should be assessed using an objective assessment method. Several performance metrics have been proposed and analyzed for MIS tasks. The differentiation of various levels of expertise is limited without the presence of an external evaluator.
METHODS: In this study, novel objective performance metrics are proposed based on mechanical energy expenditure and work. The three components of these metrics are potential energy, kinetic energy, and work. These components are optimally combined through both one-step and two-step methods. Evaluation of these metrics is accomplished for suturing and knot-tying tasks based on the performance of 30 subjects across four levels of experience.
RESULTS: The results of this study show that the one-step combined metric provides 47 and 60 accuracy in determining the level of expertise of subjects for the suturing and knot-tying tasks, respectively. The two-step combined metric provided 67 accuracy for both of the tasks studied.
CONCLUSION: The results indicate that energy expenditure is a useful metric for developing objective and efficient assessment methods. SIGNIFICANCE: These metrics can be used to evaluate and determine the proficiency levels of trainees, provide feedback and, consequently, enhance surgical simulators.

Mesh:

Year:  2017        PMID: 28541193     DOI: 10.1109/TBME.2017.2706499

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  5 in total

1.  Simulation platforms to assess laparoscopic suturing skills: a scoping review.

Authors:  Elif Bilgic; Motaz Alyafi; Tomonori Hada; Tara Landry; Gerald M Fried; Melina C Vassiliou
Journal:  Surg Endosc       Date:  2019-05-14       Impact factor: 4.584

Review 2.  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

3.  A haptic laparoscopic trainer based on affine velocity analysis: engineering and preliminary results.

Authors:  Benjamin De Witte; Charles Barnouin; Richard Moreau; Arnaud Lelevé; Xavier Martin; Christian Collet; Nady Hoyek
Journal:  BMC Surg       Date:  2021-03-18       Impact factor: 2.102

4.  Assessment of open surgery suturing skill: Simulator platform, force-based, and motion-based metrics.

Authors:  Irfan Kil; John F Eidt; Richard E Groff; Ravikiran B Singapogu
Journal:  Front Med (Lausanne)       Date:  2022-08-30

5.  Analysis of executional and procedural errors in dry-lab robotic surgery experiments.

Authors:  Kay Hutchinson; Zongyu Li; Leigh A Cantrell; Noah S Schenkman; Homa Alemzadeh
Journal:  Int J Med Robot       Date:  2022-02-14       Impact factor: 2.483

  5 in total

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