Sensor-based indicators of performance changes between sessions during robotic surgery training.,Applied Ergonomics

当前位置： X-MOL 学术 › Appl. Ergon. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Sensor-based indicators of performance changes between sessions during robotic surgery training.
Applied Ergonomics ( IF 3.1 ) Pub Date : 2020-09-19 , DOI: 10.1016/j.apergo.2020.103251
Chuhao Wu ₁ , Jackie Cha ₁ , Jay Sulek ₂ , Chandru P Sundaram ₂ , Juan Wachs ₁ , Robert W Proctor ₁ , Denny Yu ₁

Affiliation

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 ( $r_{r m} = - . 25, p < . 001$ ) and gaze entropy ( $r_{r m} = - . 37, p < . 001$ ). 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.

中文翻译：

机器人手术训练期间基于传感器的性能变化指标。

外科医生的培训对于安全有效地使用机器人手术至关重要，但目前用于学习进度的评估工具有限。这项研究的目的是测量受训者在医疗机构机器人外科医生培训课程中取得进展时认知和行为状态的变化。七名没有正式机器人培训经验的泌尿外科实习生参加了模拟课程。他们在不同的课程中重复进行 12 项不同难度的机器人技能练习。整个过程中测量了脑电图（EEG）活动和眼球运动，以计算三个指标：参与指数（任务参与度指标）、瞳孔直径（精神负荷指标）和凝视熵（凝视模式随机性指标）。每次练习后都会收集表现得分（任务目标的完成情况）和心理负荷评级（NASA-任务负荷指数）。计算训练课程之间表现得分的变化。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如何与会话之间的表现增加或减少相关。绩效的变化与参与指数的变化相关（ $r_{r 米} = - 。 25, p < 。 001$ ）和凝视熵（ $r_{r 米} = - 。 37, p < 。 001$ ）。认知和行为状态的变化能够以 72.5% 的准确率预测训练结果。研究结果表明，认知和行为指标与会话之间的表现变化相关。这些措施可以补充医学教育者和学习者当前用于机器人手术培训技能评估的反馈工具。

更新日期：2020-09-20

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11