Jonathan R Kusins1, Jason A Strelzow2, Marie-Eve LeBel2, Louis M Ferreira3,4. 1. Department of Mechanical and Materials Engineering, Western University, London, ON, N6A 5B9, Canada. 2. Roth|McFarlane Hand and Upper Limb Centre, St. Josephs Health Care, Western University, 268 Grosvenor St., London, ON, N6A 4V2, Canada. 3. Department of Mechanical and Materials Engineering, Western University, London, ON, N6A 5B9, Canada. lferreir@uwo.ca. 4. Roth|McFarlane Hand and Upper Limb Centre, St. Josephs Health Care, Western University, 268 Grosvenor St., London, ON, N6A 4V2, Canada. lferreir@uwo.ca.
Abstract
PURPOSE: Glenoid reaming is a technically challenging step during shoulder arthroplasty that could possibly be learned during simulation training. Creation of a realistic simulation using vibration feedback in this context is innovative. Our study focused on the development and internal validation of a novel glenoid reaming simulator for potential use as a training tool. METHODS: Vibration and force profiles associated with glenoid reaming were quantified during a cadaveric experiment. Subsequently, a simulator was fabricated utilizing a haptic vibration transducer with high- and low-fidelity amplifiers; system calibration was performed matching vibration peak-peak values for both amplifiers. Eight experts performed simulated reaming trials. The experts were asked to identify isolated layer profiles produced by the simulator. Additionally, experts' efficiency to successfully perform a simulated glenoid ream based solely on vibration feedback was recorded. RESULTS: Cadaveric experimental cartilage reaming produced lower vibrations compared to subchondral and cancellous bones ([Formula: see text]). Gain calibration of a lower-fidelity (3.5 [Formula: see text] and higher-fidelity (3.4 [Formula: see text] amplifier resulted in values similar to the cadaveric experimental benchmark (3.5 [Formula: see text]. When identifying random tissue layer samples, experts were correct [Formula: see text] of the time and success rate varied with tissue type ([Formula: see text]). During simulated reaming, the experts stopped at the targeted subchondral bone with a success rate of [Formula: see text]. The fidelity of the simulation did not have an effect on accuracy, applied force, or reaming time ([Formula: see text]). However, the applied force tended to increase with trial number ([Formula: see text]). CONCLUSIONS: Development of the glenoid reaming simulator, coupled with expert evaluation furthered our understanding of the role of haptic vibration feedback during glenoid reaming. This study was the first to (1) propose, develop and examine simulated glenoid reaming, and (2) explore the use of haptic vibration feedback in the realm of shoulder arthroplasty.
PURPOSE: Glenoid reaming is a technically challenging step during shoulder arthroplasty that could possibly be learned during simulation training. Creation of a realistic simulation using vibration feedback in this context is innovative. Our study focused on the development and internal validation of a novel glenoid reaming simulator for potential use as a training tool. METHODS: Vibration and force profiles associated with glenoid reaming were quantified during a cadaveric experiment. Subsequently, a simulator was fabricated utilizing a haptic vibration transducer with high- and low-fidelity amplifiers; system calibration was performed matching vibration peak-peak values for both amplifiers. Eight experts performed simulated reaming trials. The experts were asked to identify isolated layer profiles produced by the simulator. Additionally, experts' efficiency to successfully perform a simulated glenoid ream based solely on vibration feedback was recorded. RESULTS: Cadaveric experimental cartilage reaming produced lower vibrations compared to subchondral and cancellous bones ([Formula: see text]). Gain calibration of a lower-fidelity (3.5 [Formula: see text] and higher-fidelity (3.4 [Formula: see text] amplifier resulted in values similar to the cadaveric experimental benchmark (3.5 [Formula: see text]. When identifying random tissue layer samples, experts were correct [Formula: see text] of the time and success rate varied with tissue type ([Formula: see text]). During simulated reaming, the experts stopped at the targeted subchondral bone with a success rate of [Formula: see text]. The fidelity of the simulation did not have an effect on accuracy, applied force, or reaming time ([Formula: see text]). However, the applied force tended to increase with trial number ([Formula: see text]). CONCLUSIONS: Development of the glenoid reaming simulator, coupled with expert evaluation furthered our understanding of the role of haptic vibration feedback during glenoid reaming. This study was the first to (1) propose, develop and examine simulated glenoid reaming, and (2) explore the use of haptic vibration feedback in the realm of shoulder arthroplasty.
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