Development of Virtual Skill Trainers and Their Validation Study Analysis Using Machine Learning

Minimally invasive skills assessment is important in developing competent surgical simulators and executing reliable skills evaluation [9]. Arthroscopy and Laparoscopy surgeries are considered Minimally Invasive Surgeries (MIS). In MIS, the surgeon operates through small incisions with specialized narrow instruments, fiberoptic lights, and a monitor. Arthroscopy surgery is used to diagnose and treat joints problems, and Laparoscopic procedures are performed on the abdominal cavity. Due to non-natural hand-eye coordination, narrow field-of-view, and limited instrument control, MIS training is challenging to master. We are analyzing two simulators' data, Virtual Arthroscopic Tear Diagnosis and Evaluation Platform (VATDEP) and Gentleness Simulator. Both simulators went through the validation studies with human subjects. We recorded simulation data during the validation studies, such as tool motion, position, and task time. Recorded data went through the data preprocessing; after the data cleaning, we extracted the recoded data features and normalized them. Normalized features were used to input various machine learning algorithms, including K-nearest neighbor (KNN), Support vector machine (SVM), and Logistic regression (LR). The average accuracy was evaluated through k-fold cross-validation. The proposed methods validated using 10 subjects (5 experts, 5 novices) for the VATDEP simulator. 23 subjects (4 experts and 19 novices) for the Gentleness Simulator. The result shows a significant difference between the expert and novice population with the p