Abstract: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.

These metrics can be used to evaluate and determine the proficiency levels of trainees, provide feedback and, consequently, enhance surgical simulators.

No abstract

The growing popularity of minimally invasive surgery (MIS) can be attributed to its advantages, which include reduced post-operative pain, a shorter hospital stay, and faster recovery. However, MIS requires extensive training for surgeons to become experts in their field of practice. Different assessment methods have been proposed for evaluating the performance of surgeons and residents on surgical simulators. Nonetheless, optimal objective performance measures are still lacking. In this study, three metrics for minimally invasive skills assessment are proposed based on energy expenditure: work, potential energy and kinetic energy. In order to evaluate these metrics, two laparoscopic tasks consisting of suturing and knot-tying are investigated, involving expert and novice subjects. This study shows that measures based on energy expenditure can be used for skills assessment: all three metrics can discriminate between experts and novices for the two tasks investigated here. These measures can also reflect the efficiency of subjects when performing MIS tasks. Further modification and investigation of these metrics can extend their use to different tasks and for discriminating between various levels of experience.

Background: Orthopaedic training programs are incorporating arthroscopic simulations into