Using Automated Gaze-Based Feedback to Enhance Motor Skill Acquisition in Laparoscopic Surgery Training

Abstract

Laparoscopic surgery is now considered the standard of care for many abdominal procedures, offering benefits such as reduced postoperative pain, faster recovery, and shorter hospital stays. However, this type of surgery requires a distinct set of psychomotor skills that are more technically challenging than those required for open surgery, due to factors such as the separation of visual and operative fields, limited depth perception, restricted instrument movement, and motion inversion caused by the fulcrum effect. Thus, ensuring that surgical trainees acquire these skills effectively and efficiently is critical for patient safety and optimal surgical outcomes. The first stage of laparoscopic training currently relies on self-directed practice, which can be inefficient because trainees may struggle to identify and adopt strategies that effectively improve performance. To address this challenge, this research leverages eye tracking and computational techniques to examine the role of visual attention in laparoscopic skill acquisition and to evaluate personalized, gaze-based feedback for accelerating laparoscopic skill acquisition. The first study provided a foundational understanding of which eye metrics are most sensitive for early laparoscopic skill evaluation and how they reflect cognitive processes underlying performance differences. Results showed that scene-dependent eye metrics, developed relative to specific areas of interest in the visual scene, were more sensitive and more strongly correlated with performance and motion measures than scene- independent metrics, highlighting their value for assessing early skill development. Building on these findings, the second study employed scene-dependent eye metrics to track changes in visual attention as novices developed technical proficiency and to compare gaze behaviors between fast and slow learners. The results showed that better performance and faster learning were linked to more efficient gaze behaviors, laying the groundwork for the final study. The final study employed a custom software application to investigate timely gaze-based feedback on skill acquisition. Unlike prior gaze-training approaches that relied on human instructors for continuous verbal feedback, this application analyzed eye gaze data, computed eye metrics for specific gaze behaviors, compared them with benchmark values, and provided actionable feedback. Results demonstrated that the system could improve laparoscopic skill acquisition, particularly during early training stages. Taken together, these studies demonstrate the feasibility and promise of using automated eye-tracking feedback to enhance laparoscopic skill evaluation and training. By integrating advanced eye tracking with AI-driven video and data processing, this work represents a critical first step toward developing scalable, cost-effective virtual coaching systems capable of personalizing formative feedback, accelerating psychomotor skill acquisition, and optimizing surgical training outcomes.