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Sensors 2017, 17(8), 1808;

Energy-Based Metrics for Arthroscopic Skills Assessment

Canadian Surgical Technologies and Advanced Robotics (CSTAR), London, ON N6A 5A5, Canada
Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
Department of Surgery, Western University, London, ON N6A 4V2, Canada
Department of Mechanical and Materials Engineering, Western University, London, ON N6A 5B9, Canada
Author to whom correspondence should be addressed.
Received: 18 June 2017 / Revised: 14 July 2017 / Accepted: 29 July 2017 / Published: 5 August 2017
(This article belongs to the Special Issue Force and Pressure Based Sensing Medical Application)
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Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency. View Full-Text
Keywords: energy-based metrics; surgical skills assessment; arthroscopy; machine learning algorithms; sensorized instruments energy-based metrics; surgical skills assessment; arthroscopy; machine learning algorithms; sensorized instruments

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Poursartip, B.; LeBel, M.-E.; McCracken, L.C.; Escoto, A.; Patel, R.V.; Naish, M.D.; Trejos, A.L. Energy-Based Metrics for Arthroscopic Skills Assessment. Sensors 2017, 17, 1808.

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