The aim of this proposal is to design, develop, and validate Virtual Rotator Cuff Arthroscopic Skill Trainer (ViRCAST) to virtually simulate arthroscopic rotator cuff repair surgery and perform extensive validation studies to demonstrate its effectiveness as a training platform. The platform will provide safe training environment to reduce training time for acquisition of necessary skills and provide objective skill learning process with quantitative measures. The ViRCAST will incorporate structured and customized learning (e.g. trainee specific learning), which is expected to improve trainee's psychomotor skills while providing cognitive feedback. It will also assist training with increasing difficulty scenarios. We hypothesize that the proposed platform would significantly improve training with deliberate and individual specific learning. ViRCAST will be unique and innovative by incorporating; (1) High-fidelity visualization of the bone, tissue, and instruments, (2) realistic physics-based soft tissue and bone interactions with instruments and fluid and electrocautery simulation, (3) physics-based tissue and bone haptic (touch) sensation during the bone shaving and cleaning tasks, (4) use of actual instruments with hardware interfaces (5) simulation of knot tying tasks (6) cognitive skill training on various difficulty settings (7) rigorous validation studies (e.g. content, concurrent, discriminant, deliberate learning assessment) with human subjects, (8) Objective metrics derived from the clinical practice and (9) personalization in training to detect and eliminate the deficiencies and bad habits. ViRCAST will incorporate personalized training (e.g. deliberate learning) that will pinpoint trainees' skill deficiencies and aim to improve their skill levels. The ViRCAST will be providing standardized, quantified feedback for evaluation and self-assessment by measuring (a) the accuracy and smoothness of surgeons' hand movements (hand-eye coordination), (b) accuracy of the applied procedure (e.g. anchor angle, distribution of knots, drilling depth), (c) accidental damage or errors (e.g. injuring anatomical structures), (d) performance in conducting a specific task within the surgery (e.g. hemostasis duration), and (e) cognitive decision making process for the complex scenarios. With such a novel training technology, it will also be possible to track trainees' performance trends over time and personalize the training sequence by creating dynamic scenes to improve trainees' deficiency areas.
We propose a virtual arthroscopic skill trainer for rotator cuff surgery that will help surgical residents in skill and knowledge acquisition in a deliberate and trainee-specific learning environment. The proposed trainer will provide various modalities such as physics based interactive simulation, realistic visualization and haptic feedback. We will perform face, content, construct, and discriminant validation studies as well as deliberate and individual specific learning assessment tests to establish effectiveness of our simulator in arthroscopy training.
