Open AccessDetecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking
Author(s) -
Xin Liu,
Bin Zheng,
Xiaoqin Duan,
Wenjing He,
Yuandong Li,
Jinyu Zhao,
ChongKe Zhao,
Lin Wang
Publication year - 2021
Publication title -
journal of eye movement research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.25
H-Index - 20
ISSN - 1995-8692
DOI - 10.16910/jemr.14.2.5
Subject(s) - computer science , artificial intelligence , eye tracking , gaze , deep learning , eye movement , reinforcement learning , machine learning , health care , convolutional neural network , adversarial system , computer vision , economics , economic growth
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.