Purdue University, West Lafayette, IN, United States.
Indiana University, Indianapolis, IN, United States.
Appl Ergon. 2021 Jan;90:103251. doi: 10.1016/j.apergo.2020.103251. Epub 2020 Sep 19.
Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees' cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (r=-.25,p<.001) and gaze entropy (r=-.37,p<.001). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.
外科医生的培训对于机器人手术的安全和有效使用至关重要，但目前用于学习进展的评估工具有限。本研究的目的是测量外科受训者在医疗机构的机器人外科培训课程中的认知和行为状态的变化。7 名泌尿科无正式机器人培训经验的外科受训者参与了模拟课程。他们在单独的课程中重复进行了 12 项具有不同难度水平的机器人技能练习。在整个过程中测量脑电图 (EEG) 活动和眼球运动，以计算三个指标：参与指数（任务参与度的指标）、瞳孔直径（心理工作量的指标）和注视熵（注视模式随机性的指标）。在每次练习后收集绩效评分（完成任务目标）和心理工作量评分（NASA 任务负荷指数）。计算训练课程之间的绩效评分变化。方差分析、重复测量相关性和机器学习分类用于诊断认知和行为状态与课程之间的绩效提高或下降之间的关联。绩效的变化与参与指数（r=-.25，p<.001）和注视熵（r=-.37，p<.001）的变化相关。认知和行为状态的变化可以以 72.5%的准确率预测培训结果。研究结果表明，认知和行为指标与课程之间的绩效变化相关。这些措施可以补充当前医学教育者和学习者在机器人手术培训中用于技能评估的反馈工具。
