Detecting performance 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 requires 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 per...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Journal of Eye Movement Research |
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| Online Access: | https://bop.unibe.ch/JEMR/article/view/7515 |
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| _version_ | 1849760966127910912 |
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| author | Xin Liu Bin Zheng Xiaoqin Duan Wenjing He Yuandong Li Jinyu Zhao Chen Zhao Lin Wang |
| author_facet | Xin Liu Bin Zheng Xiaoqin Duan Wenjing He Yuandong Li Jinyu Zhao Chen Zhao Lin Wang |
| author_sort | Xin Liu |
| collection | DOAJ |
| description | Eye-tracking can help decode the intricate control mechanism in human performance. In healthcare, physicians-in-training requires 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. The 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 applied deep learning algorithms to detect the eye-tracking metrics on the moments of navigation lost (MNL), a signature sign for performance difficulty during colonoscopy. Basic 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 (90%), sensitivity (90%), and specificity (88%) were optimized. This study built an important foundation for our work of developing a self-adaptive education system for training healthcare skills using simulation. |
| format | Article |
| id | doaj-art-4e29f13d30a04b97a20fedd8bb894933 |
| institution | DOAJ |
| issn | 1995-8692 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Eye Movement Research |
| spelling | doaj-art-4e29f13d30a04b97a20fedd8bb8949332025-08-20T03:06:10ZengMDPI AGJournal of Eye Movement Research1995-86922021-07-0114210.16910/jemr.14.2.5Detecting performance difficulty of learners in colonoscopy: Evidence from eye-trackingXin Liu0Bin Zheng1Xiaoqin Duan2Wenjing He3Yuandong Li4Jinyu Zhao5Chen Zhao6Lin Wang7University of Science and Technology Beijing; University of AlbertaUniversity of AlbertaJilin University Second Hospital; University of AlbertaUniversity of ManitobaShanxi Bethune HospitalUniversity of AlbertaUniversity of Science and Technology Beijing; Beijing Key Laboratory of Knowledge Engineering for Materials ScienceUniversity of AlbertaEye-tracking can help decode the intricate control mechanism in human performance. In healthcare, physicians-in-training requires 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. The 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 applied deep learning algorithms to detect the eye-tracking metrics on the moments of navigation lost (MNL), a signature sign for performance difficulty during colonoscopy. Basic 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 (90%), sensitivity (90%), and specificity (88%) were optimized. This study built an important foundation for our work of developing a self-adaptive education system for training healthcare skills using simulation.https://bop.unibe.ch/JEMR/article/view/7515colonoscopysimulationeye-trackingnavigationDeep Convolutional Generative Adversarial Networks (DCGANs)Long Short-Term Memory (LSTM) networks |
| spellingShingle | Xin Liu Bin Zheng Xiaoqin Duan Wenjing He Yuandong Li Jinyu Zhao Chen Zhao Lin Wang Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking Journal of Eye Movement Research colonoscopy simulation eye-tracking navigation Deep Convolutional Generative Adversarial Networks (DCGANs) Long Short-Term Memory (LSTM) networks |
| title | Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking |
| title_full | Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking |
| title_fullStr | Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking |
| title_full_unstemmed | Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking |
| title_short | Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking |
| title_sort | detecting performance difficulty of learners in colonoscopy evidence from eye tracking |
| topic | colonoscopy simulation eye-tracking navigation Deep Convolutional Generative Adversarial Networks (DCGANs) Long Short-Term Memory (LSTM) networks |
| url | https://bop.unibe.ch/JEMR/article/view/7515 |
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