结肠镜检查中学习者任务难度的检测:来自眼动追踪的证据。

IF 1.3 4区 心理学 Q3 OPHTHALMOLOGY
Journal of Eye Movement Research Pub Date : 2021-07-13 eCollection Date: 2021-01-01 DOI:10.16910/jemr.14.2.5
Liu Xin, Zheng Bin, Duan Xiaoqin, He Wenjing, Li Yuandong, Zhao Jinyu, Zhao Chen, Wang Lin
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引用次数: 3

摘要

眼球追踪可以帮助解读人类行为中复杂的控制机制。在医疗保健领域,实习医生需要广泛的实践来提高他们的医疗技能。当学员在练习中遇到困难时,他们需要专家的反馈来提高他们的表现。个人反馈既费时又容易受到偏见的影响。在这项研究中,我们在模拟中跟踪了受训人员在结肠镜检查过程中的眼球运动。我们研究了结肠镜检查过程中导航丢失时刻(MNL)的眼动行为变化,并测试了深度学习算法是否可以通过输入眼动追踪数据来检测MNL。采用深度卷积生成对抗网络(dcgan)学习并验证人眼凝视和瞳孔特征;将生成的数据以三种不同的数据馈送策略馈送到长短期记忆(LSTM)网络,以对整个结肠镜检查过程中的mnl进行分类。将深度学习的输出与专家基于结肠镜视频对mnl的判断进行比较。用1000张人眼合成数据进行分类,准确率(91.80%)、灵敏度(90.91%)、特异度(94.12%)达到最佳。本研究为我们开发模拟医疗技能培训教育系统的工作奠定了重要基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Detecting Task Difficulty of Learners in Colonoscopy: Evidence from Eye-Tracking.

Detecting Task Difficulty of Learners in Colonoscopy: Evidence from Eye-Tracking.

Detecting Task Difficulty of Learners in Colonoscopy: Evidence from Eye-Tracking.

Detecting Task Difficulty of Learners in Colonoscopy: Evidence from Eye-Tracking.

Eye-tracking can help decode the intricate control mechanism in human performance. In healthcare, physicians-in-training require extensive practice to improve their healthcare skills. When a trainee encounters any difficulty in the practice, they will need feedback from experts to improve their performance. Personal feedback is time-consuming and subjected to bias. In this study, we tracked the eye movements of trainees during their colonoscopic performance in simulation. We examined changes in eye movement behavior during the moments of navigation loss (MNL), a signature sign for task difficulty during colonoscopy, and tested whether deep learning algorithms can detect the MNL by feeding data from eye-tracking. Human eye gaze and pupil characteristics were learned and verified by the deep convolutional generative adversarial networks (DCGANs); the generated data were fed to the Long Short-Term Memory (LSTM) networks with three different data feeding strategies to classify MNLs from the entire colonoscopic procedure. Outputs from deep learning were compared to the expert's judgment on the MNLs based on colonoscopic videos. The best classification outcome was achieved when we fed human eye data with 1000 synthesized eye data, where accuracy (91.80%), sensitivity (90.91%), and specificity (94.12%) were optimized. This study built an important foundation for our work of developing an education system for training healthcare skills using simulation.

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来源期刊
CiteScore
2.90
自引率
33.30%
发文量
10
审稿时长
10 weeks
期刊介绍: The Journal of Eye Movement Research is an open-access, peer-reviewed scientific periodical devoted to all aspects of oculomotor functioning including methodology of eye recording, neurophysiological and cognitive models, attention, reading, as well as applications in neurology, ergonomy, media research and other areas,
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