Data analytics interrogates robotic surgical performance using a microsurgery-specific haptic device

With the increase in robot-assisted cases, recording the quantifiable dexterity of surgeons is essential for proficiency evaluations. The present study employs sensor-based kinematics and recorded surgeon experience for evaluating a new haptic device. Thirty surgeons performed a task simulating micromanipulation with neuroArmPLUSHD and two commercially available hand-controllers. The surgical performance was evaluated based on subjective measures obtained from survey and objective features derived from the sensors. Statistical analyses were performed to assess the hand-controllers and regression analysis was used to identify the key features and develop a machine learning model for surgical skill assessment. MANCOVA tests on objective features demonstrated significance (α = 0.05) for time (p = 0.02), errors (p = 0.01), distance (p = 0.03), clutch incidents (p = 0.03), and forces (p = 0.00). The majority of metrics were in favor of neuroArmPLUSHD. The surgeons found it smoother, more comfortable, less tiring, and easier to maneuver with more realistic force feedback. The ensemble machine learning model trained with 5-fold cross-validation showed an accuracy (SD) of 0.78 (0.15) in surgeon skill classification. This study validates the importance of incorporating a superior haptic device in telerobotic surgery for standardization of surgical education and patient care.