Skills Assessment in Arthroscopic Surgery by Processing Kinematic, Force, and Bio-signal Data

Arthroscopic surgery is a type of Minimally Invasive Surgery (MIS) performed in human joints, which can be used for diagnostic or treatment purposes. The nature of this type of surgery makes it such that surgeons require extensive training to become experts at performing surgical tasks in tight environments and with reduced force feedback. MIS increases the possibility of erroneous actions, which could result in injury to the patient. Many of these injuries can be prevented by implementing appropriate training and skills assessment methods. Various performance methods, including Global Rating Scales and technical measures, have been proposed in the literature. However, there is still a need to further improve the accuracy of surgical skills assessment and improve its ability to distinguish fine variations in surgical proficiency. The main goal of this thesis is to enhance surgical, and specifically, arthroscopic skills assessment. The optimal assessment method should be objective, distinguish between subjects with different levels of expertise, and be computationally efficient. This thesis proposes a new method of investigating surgical skills by introducing energy expenditure metrics. To this end, two main approaches are pursued: 1) evaluating the kinematics of instrument motion, and 2) exploring the muscle activity of trainees. Mechanical energy expenditure and work are investigated for a variety of laparoscopic and arthroscopic tasks. The results obtained in this thesis demonstrate that expert surgeons expend less energy than novice trainees. The different forms of mechanical energy expenditure were combined through optimization methods and machine learning algorithms. An optimum two-step optimization method for classifying trainees into detailed levels of expertise is proposed that demonstrates an enhanced ability to determine the level of expertise of trainees compared to other published methods. Furthermore, performance metrics are proposed based on electromyography signals of the forearm muscles, which are recorded using a wearable device. These results also demonstrate that the metrics defined based on muscle activity can be used for arthroscopic skills assessment. The energy-based metrics and the muscle activity metrics demonstrated the ability to identify levels of expertise, with accuracy levels as high as 95% and 100%, respectively. The primary contribution of this thesis is the development of novel metrics and assessment methods based on energy expenditure and muscle activity. The methods presented advance our knowledge of the characteristics of dexterous performance and add another perspective to quantifying surgical proficiency

Energy-based metrics for arthroscopic skills assessment

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

Energy-based metrics for laparoscopic skills assessment

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

Analysis of Energy-Based Metrics for Laparoscopic Skills Assessment

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

Performance evaluation of a sensorized arthroscopic grasper

Force sensing minimally invasive instruments have gained increasing attention in recent years. Integrating these instruments within currently available surgical simulators can enhance the learning experience by measuring the forces applied by trainees and supplementing objective performance assessment. Recently, an arthroscopic grasper was designed and sensorized with Fiber Bragg Grating Sensors at Canadian Surgical Technologies and Advanced Robotics (CSTAR). Moreover, a custom low-cost (LC) interrogation system was developed to accompany the proposed sensorized tool. In this study, the custom LC interrogator was used and compared to the commerciallyavailable Micron Optics sm130 (MO) interrogator. The hypothesis is that both of these systems can be used to measure forces within 0:5 N as the acceptable margin for accuracy. Experimental results showed that the MO system meets the required accuracy for certain force directions. The LC system demonstrated 49% of the accuracy of the MO interrogator. The main advantage of the LC interrogator is its cost, which is 18% of the commercial interrogation system. For certain force directions, the performance was comparable to the defined criteria

Development of a physical shoulder simulator for the training of basic arthroscopic skills

Background: Orthopaedic training programs are incorporating arthroscopic simulations into their residency curricula. There is a need for a physical shoulder simulator that accommodates lateral decubitus and beach chair positions, has realistic anatomy, allows for an objective measure of performance and provides feedback to trainees. Methods: A physical shoulder simulator was developed for training basic arthroscopic skills. Sensors were embedded in the simulator to provide a means to assess performance. Subjects of varying skill level were invited to use the simulator and their performance was objectively assessed. Results: Novice subjects improved their performance after practice with the simulator. A survey completed by experts recognized the simulator as a valuable tool for training basic arthroscopic skills. Conclusions: The physical shoulder simulator helps train novices in basic arthroscopic skills and provides objective measures of performance. By using the physical shoulder simulator, residents could improve their basic arthroscopic skills, resulting in improved patient safety