The article is tited “Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training” and is available at https://doi.org/10.1177/0018720819874544. First author Chuhao Wu is a graduate MS Thesis student in Dr. Denny Yu's HEAL group. Other authors are Jackie Cha, Jay Sulek, Tian Zhou, Chandru P. Sundaram, Juan Wachs, and Denny Yu.

Robotic techniques provide improved dexterity, stereoscopic vision, and ergonomic control system over laparoscopic surgery, but the complexity of the interfaces and operations may pose new challenges to surgeons and compromise patient safety. Limited studies have objectively quantified workload and its impact on performance in robotic surgery. Although not yet implemented in robotic surgery, minimally intrusive and continuous eye-tracking metrics have been shown to be sensitive to changes in workload in other domains.

Eye-tracking measures can detect perceived workload during robotic tasks. They can potentially be used to identify task contributors to high workload and provide measures for robotic surgery training.