See Like an Expert: Gaze-Augmented Training Enhances Skill Acquisition in a Virtual Reality Robotic Suturing Task

摘要

Introduction: The da Vinci Skills Simulator (DVSS) is an effective platform for robotic skills training. Novel training methods using expert gaze patterns to guide trainees have demonstrated superiority to traditional instruction. Portable head-mounted eye-trackers (HMET) offer the opportunity for eye tracking technology to enhance surgical robotic simulation training. Objective: To evaluate if training guided by expert gaze patterns can improve trainee performance over standard movement training techniques during robotic simulation. Methods: Medical students were recruited and randomized into gaze training (GT, n = 9) and movement training (MT, n = 8) groups. First, the participants reviewed an instructional video, with the GT group emulating expert gaze patterns and the MT group ( n = 8) standard movement-based instruction. Training consisted of 10 repetitions of “Suture Sponge 3” on the DVSS while wearing HMET; the first three repetitions were followed by group-appropriate video coaching (gaze vs movement feedback), while the remaining repetitions were without feedback. Finally, two multitasking repetitions with a secondary bell-counting task were completed. Primary outcomes included DVSS scores during training and multitasking. Secondary outcomes included metrics collected from the HMET (gaze patterns and gaze entropy). Results: Total score, efficiency, and penalties improved significantly over the training in both groups; the GT group achieved higher scores on every attempt. Total scores in the GT group were higher than the MT group postvideo review (20.3 ± 21.8 vs 3.0 ± 6.2, p = 0.047), after coaching repetitions (61.8 ± 18.8 vs 30.1 ± 26.2, p = 0.01), and at the last training attempt (73.0 ± 16.5 vs 63.1 ± 17.4, p = 0.247). During multitasking, the GT group maintained higher total scores (75 ± 10.1 vs 63.3 ± 15.3, p = 0.01), efficiency (86.3 ± 7.4 vs 77.4 ± 11.2, p = 0.009), and superior secondary task performance (error: 6.3% ± 0.06 vs 10.7% ± 0.11, p = 0.20). Gaze entropy (cognitive-load indicator) and gaze pattern analysis showed similar trends. Conclusion: Gaze-augmented training leads to more efficient movements through adoption of expert gaze patterns that withstand additional stressors.