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

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Abstract

Background

Objective assessment of psychomotor skills has become an important challenge in the training of minimally invasive surgical (MIS) techniques. Currently, no gold standard defining surgical competence exists for classifying residents according to their surgical skills. Supervised classification has been proposed as a means for objectively establishing competence thresholds in psychomotor skills evaluation. This report presents a study comparing three classification methods for establishing their validity in a set of tasks for basic skills’ assessment.

Methods

Linear discriminant analysis (LDA), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) were used. A total of 42 participants, divided into an experienced group (4 expert surgeons and 14 residents with >10 laparoscopic surgeries performed) and a nonexperienced group (16 students and 8 residents with <10 laparoscopic surgeries performed), performed three box trainer tasks validated for assessment of MIS psychomotor skills. Instrument movements were captured using the TrEndo tracking system, and nine motion analysis parameters (MAPs) were analyzed. The performance of the classifiers was measured by leave-one-out cross-validation using the scores obtained by the participants.

Results

The mean accuracy performances of the classifiers were 71 % (LDA), 78.2 % (SVM), and 71.7 % (ANFIS). No statistically significant differences in the performance were identified between the classifiers.

Conclusions

The three proposed classifiers showed good performance in the discrimination of skills, especially when information from all MAPs and tasks combined were considered. A correlation between the surgeons’ previous experience and their execution of the tasks could be ascertained from results. However, misclassifications across all the classifiers could imply the existence of other factors influencing psychomotor competence.

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Acknowledgments

The authors thank all the surgeons, residents, and medical students who kindly volunteered and participated in the clinical trials, as well as the staff of the skills laboratory at the Leiden University Medical Centre for providing the available working space.

Disclosures

Ignacio Oropesa, Patricia Sánchez-González, Frank Willem Jansen, Jenny Dankelman, and Enrique J. Gómez participate under partial funding of LLP-Leonardo da Vinci project MISTELA (528125-LLP-1-2012-1-UK). Magdalena K. Chmarra, Pablo Lamata, and Rodrigo Pérez-Rodríguez have no conflicts of interest or financial ties to disclose.

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Authors and Affiliations

  1. Bioengineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Avda Complutense, 30, 28040, Madrid, Spain

    Ignacio Oropesa, Patricia Sánchez-González, Pablo Lamata, Rodrigo Pérez-Rodríguez & Enrique J. Gómez

  2. Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Poeta Mariano Esquillor s/n, 50018, Zaragoza, Spain

    Ignacio Oropesa, Patricia Sánchez-González, Rodrigo Pérez-Rodríguez & Enrique J. Gómez

  3. Department of Circulation and Medical Imaging, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Magdalena K. Chmarra

  4. Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands

    Frank Willem Jansen & Jenny Dankelman

  5. Department of Gynecology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands

    Frank Willem Jansen

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  1. Ignacio Oropesa
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  2. Patricia Sánchez-González
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  3. Magdalena K. Chmarra
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  4. Pablo Lamata
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  8. Enrique J. Gómez
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Corresponding author

Correspondence to Ignacio Oropesa.

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Oropesa, I., Sánchez-González, P., Chmarra, M.K. et al. Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis. Surg Endosc 28, 657–670 (2014). https://doi.org/10.1007/s00464-013-3226-7

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