Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = educational output
Page = 2

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
13 pages, 8618 KB  
Article
Detecting Task Difficulty of Learners in Colonoscopy: Evidence from Eye-Tracking
by Liu Xin, Zheng Bin, Duan Xiaoqin, He Wenjing, Li Yuandong, Zhao Jinyu, Zhao Chen and Wang Lin
J. Eye Mov. Res. 2021, 14(2), 1-13; https://doi.org/10.16910/jemr.14.2.5 - 13 Jul 2021
Cited by 14 | Viewed by 387
Abstract
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 [...] Read more.
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. Full article
Show Figures

Figure 1

Back to TopTop