School of Computing, Jordanstown Campus, Ulster University, Shore Road, Newtownabbey, BT37 0QB, Northern Ireland, UK.
School of Nursing, Magee Campus, Ulster University, Londonderry, BT48 7JL, Northern Ireland, UK.
Int J Comput Assist Radiol Surg. 2019 Apr;14(4):645-657. doi: 10.1007/s11548-019-01918-0. Epub 2019 Feb 7.
Unobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure.
Two aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity).
Experts provided greater dwell time (63% vs 42%, p = 0.03) and fixations (50% vs 34%, p = 0.04) on display screens. They also provided greater dwell time (11% vs 5%, p = 0.006) and fixations (9% vs 4%, p = 0.007) when selecting instruments. The experts' performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r = - 0.95, p = 0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p = 0.04).
Eye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity. Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.
能够在临床操作过程中自动评估性能的非侵入性指标很有价值。探索了三种从可穿戴技术中获取指标的方法：（1）眼球追踪，（2）心理生理测量[例如皮肤电活动（EDA）]和（3）通过加速度计测量手臂和手部运动。我们还通过让操作员在手术过程中执行额外的任务来跟踪不相关的物体，从而测量注意力容量。
测量两个方面的性能：（1）使用眼动和心理生理指标，（2）通过执行额外的不相关任务（监视视觉刺激/玩纸牌）来测量注意力容量。目的是确定可用于自动区分性能水平或至少区分新手和专家的指标。该研究使用两组进行：（1）新手操作员和（2）专家操作员。两组都使用全物理虚拟现实模拟器进行两次冠状动脉造影程序尝试。参与者佩戴眼动追踪眼镜和 E4 可穿戴腕带。定义了感兴趣区域以在显示屏幕上跟踪视觉注意力，包括：（1）X 射线，（2）生命体征，（3）仪器和（4）刺激屏幕（用于测量注意力容量）。
专家在显示屏幕上提供了更多的停留时间（63%对 42%，p=0.03）和注视（50%对 34%，p=0.04）。他们在选择仪器时还提供了更多的停留时间（11%对 5%，p=0.006）和注视（9%对 4%，p=0.007）。专家在执行视觉刺激任务时跟踪不相关物体的表现与总错误呈负相关（r=-0.95，p=0.0009）。专家的 EDA 标准偏差也更高（2.52 µS 对 0.89 µS，p=0.04）。
眼球追踪指标可以通过显示专家在显示屏幕上保持更高的视觉注意力来帮助区分新手和专家操作员。此外，视觉刺激研究表明，不相关的任务可以测量注意力容量。
这项工作通过美国国立卫生研究院的服务机构 clinicaltrials.gov 进行注册，并通过试验参考号进行识别：NCT02928796。
