INTRODUCTION In laparoscopic surgery, looking in the target areas is an indicator of proficiency. However, gaze behaviors revealing feedforward control (i.e., looking ahead) and their importance have been under-investigated in surgery. This study aims to establish the sensitivity and relative importance of different scene-dependent gaze and motion metrics for estimating trainee proficiency levels in surgical skills. METHODS Medical students performed the Fundamentals of Laparoscopic Surgery peg transfer task while recording their gaze on the monitor and tool activities inside the trainer box. Using computer vision and fixation algorithms, five scene-dependent gaze metrics and one tool speed metric were computed for 499 practice trials. Cluster analysis on the six metrics was used to group the trials into different clusters/proficiency levels, and ANOVAs were conducted to test differences between proficiency levels. A Random Forest model was trained to study metric importance at predicting proficiency levels. RESULTS Three clusters were identified, corresponding to three proficiency levels. The correspondence between the clusters and proficiency levels was confirmed by differences between completion times (F2,488 = 38.94, p < .001). Further, ANOVAs revealed significant differences between the three levels for all six metrics. The Random Forest model predicted proficiency level with 99% out-of-bag accuracy and revealed that scene-dependent gaze metrics reflecting feedforward behaviors were more important for prediction than the ones reflecting feedback behaviors. CONCLUSION Scene-dependent gaze metrics revealed skill levels of trainees more precisely than between experts and novices as suggested in the literature. Further, feedforward gaze metrics appeared to be more important than feedback ones at predicting proficiency.

中文翻译：

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依赖于场景的前馈眼睛注视指标可以区分腹腔镜手术学员的技术技能水平。


简介 在腹腔镜手术中，观察目标区域是熟练程度的指标。然而，揭示前馈控制（即向前看）的凝视行为及其重要性在手术中尚未得到充分研究。本研究旨在确定不同场景相关注视和运动指标的敏感性和相对重要性，以估计学员的手术技能熟练程度。方法 医学生执行腹腔镜手术基础钉转移任务，同时记录他们对监视器的注视以及训练箱内的工具活动。使用计算机视觉和注视算法，为 499 次实践试验计算了 5 个与场景相关的注视指标和 1 个工具速度指标。对六个指标进行聚类分析，将试验分组为不同的聚类/熟练程度，并进行方差分析来测试熟练程度之间的差异。训练随机森林模型来研究预测熟练程度的指标重要性。结果 确定了三个集群，对应于三个熟练程度。集群和熟练程度之间的对应关系通过完成时间之间的差异得到证实 (F2,488 = 38.94, p < .001)。此外，方差分析揭示了所有六个指标的三个级别之间的显着差异。随机森林模型以 99% 的袋外准确率预测熟练程度，并表明反映前馈行为的场景相关注视指标比反映反馈行为的预测更重要。结论 与文献中建议的专家和新手之间相比，场景相关的注视指标更准确地揭示了受训者的技能水平。 此外，在预测熟练程度方面，前馈注视指标似乎比反馈指标更重要。