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.

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.

Mastery Learning - does the method of learning make a difference in skills acquisition for robotic surgery?

BACKGROUND: Few studies compare the effectiveness of blocked vs random practice conditions in minimally invasive surgery training, and none have evaluated these in robotic surgery training. METHODS: The dV-Trainer® and the da Vinci® Surgical System (dVSS) were used to compare practice conditions. Forty-two participants were randomized into blocked and random practice groups. Each participant performed five tasks: Ring Walk, Thread the Rings, Needle Targeting, Suture Sponge and Tubes Level 2. Transfer to the dVSS was also assessed. RESULTS: No significant differences were observed between the two groups, except for a few instances. For example, during Ring Walk, the random group performed significantly faster than the blocked group (100.78 ± 5.26 s vs 121.59 ± 5.26 s, p < 0.01). CONCLUSIONS: The study results do not follow the current evidence presented in the education literature. This is the first time that blocked versus random practice was tested for robotic surgery training.

An autoclavable wireless palpation instrument for minimally invasive surgery.

Minimally invasive surgery prevents surgeons from manually palpating organs to locate subsurface tumors and other structures. One solution is to use ultrasound; however, it is not always reliable. Various minimally invasive surgery instruments that provide tactile feedback have been proposed to augment ultrasound sensing for tumor localization; however, current designs have limitations such as cumbersome wiring, difficulty in manipulation, lack of sterilizability and high cost. This paper presents a novel, autoclavable, wireless, hand-held palpation instrument that uses a custom, low-cost, disposable tactile sensor to provide tactile and kinesthetic force feedback. The use of a replaceable, disposable tactile sensor avoids deterioration in sensor performance due to repeated autoclaving. The instrument features a passive joint in the end effector that allows the sensor to self-align to the palpation surface in a wide range of orientations. All of the electronics are packaged in a removable module that allows the rest of the instrument to be easily cleaned and autoclaved. Two versions of the tactile sensor, using piezoresistive sensing and capacitive sensing respectively, have been designed for use with this instrument. The instrument is shown to be able to detect 6 mm diameter spherical tumors at a depth of 9-10 mm in ex vivo tissue samples.

Anatomy-based eligibility measure for robotic-assisted bypass surgery.

OBJECTIVE: Robotic-assisted endoscopic single-vessel small thoracotomy allows clinicians to perform coronary artery bypass grafting surgery in a minimally invasive manner using the da Vinci Surgical System. Not all patients are suitable for this technique, and the lack of an appropriate method for patient eligibility avoids completion of the procedure robotically. The objective of this study was to develop a patient eligibility method based on the anatomy of the chest of the patient. METHODS: Preoperative computed tomography thorax scans of 110 patients were analyzed. Two-dimensional measurements taken on the axial images were used with the goal of finding a relation between the anatomy of the patient and the completion of the procedure robotically. RESULTS: Patients with a distance from the left anterior descending coronary artery to the anterior chest wall of smaller than 15 mm have a 20% probability of requiring conversion of the procedure to open surgery. This probability increases if the chest of the patient is very elliptical, having an anterior-posterior dimension of less than 45% of the transverse dimension. CONCLUSIONS: The smaller the distance is from the left anterior descending artery to the anterior chest wall, the lower the chances are of completing the procedure robotically.

A knee arthroscopy simulator: design and validation.

Many challenges exist when teaching and learning arthroscopic surgery, carrying a high risk of damaging the joint during the learning process. To minimize risk, the use of arthroscopy simulators allows trainees to learn basic skills in a risk-free environment before entering the operating room. A high-fidelity physical knee arthroscopy simulator is proposed to bridge the gap between surgeons and residents. The simulator is composed of modular and replaceable elements and can measure applied forces, instrument position and hand motion, in order to assess performance in real time. A construct validity study was conducted in order to assess the performance improvement of novices after practicing with the simulator. In addition, a face validity study involving expert surgeons indicated that the simulator provides a realistic scenario suitable for teaching basic skills. Future work involves the development of better metrics to assess user performance.

Force sensing-based simulator for arthroscopic skills assessment in orthopaedic knee surgery.

The complexity of knee arthroscopy makes it difficult to teach and assess skill level during real surgery. Simulator-based training is ideal for this complex procedure. To address the limitations of existing systems, a physical simulator, capable of providing skills assessment and feedback has been developed. The simulator measures the forces applied on the femur and acting on the tools. An experimental evaluation was conducted to assess the differences in task completion time and applied forces for fourteen tasks performed by trainees and expert surgeons. Initial results show that the simulator, together with well-chosen tasks, can potentially be used to assess user performance.