Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition
Abstract Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces the adenoma miss rate (AMR). However, few studies address the critical issue of endoscopist-AI collaboration in real-world settings. Eye-track...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04535-6 |
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| author | Yan Zhu Rui-Jie Yang Pei-Yao Fu Zhen Zhang Yi-Zhe Zhang Quan-Lin Li Shuo Wang Ping-Hong Zhou |
| author_facet | Yan Zhu Rui-Jie Yang Pei-Yao Fu Zhen Zhang Yi-Zhe Zhang Quan-Lin Li Shuo Wang Ping-Hong Zhou |
| author_sort | Yan Zhu |
| collection | DOAJ |
| description | Abstract Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces the adenoma miss rate (AMR). However, few studies address the critical issue of endoscopist-AI collaboration in real-world settings. Eye-tracking data collection is considered a promising approach to uncovering how endoscopists and AI interact and influence each other during colonoscopy procedures. A common limitation of existing studies is their reliance on retrospective video clips, which fail to capture the dynamic demands of real-time colonoscopy, where endoscopists must simultaneously navigate the colonoscope and identify lesions on the screen. To address this gap, we established a dataset to analyze changes in endoscopists’ eye movements during the colonoscopy withdrawal phase. Eye-tracking data was collected from graduate students, nurses, senior endoscopists, and novice endoscopists while they reviewed retrospectively recorded colonoscopy withdrawal videos, both with and without computer-aided detection (CADe) assistance. Furthermore, 80 real-time video segments were prospectively collected during endoscopists’ actual colonoscopy withdrawal procedures, comprising 43 segments with CADe assistance and 37 segments without assistance (normal control). |
| format | Article |
| id | doaj-art-ce2f149ee06e4d59927cb76b5d36a83c |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-ce2f149ee06e4d59927cb76b5d36a83c2025-02-09T12:11:41ZengNature PortfolioScientific Data2052-44632025-02-011211910.1038/s41597-025-04535-6Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisitionYan Zhu0Rui-Jie Yang1Pei-Yao Fu2Zhen Zhang3Yi-Zhe Zhang4Quan-Lin Li5Shuo Wang6Ping-Hong Zhou7Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan UniversityShanghai Collaborative Innovation Center of EndoscopyEndoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan UniversityEndoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan UniversitySchool of Computer Science and Engineering, Nanjing University of Science and TechnologyEndoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan UniversityShanghai Collaborative Innovation Center of EndoscopyEndoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan UniversityAbstract Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces the adenoma miss rate (AMR). However, few studies address the critical issue of endoscopist-AI collaboration in real-world settings. Eye-tracking data collection is considered a promising approach to uncovering how endoscopists and AI interact and influence each other during colonoscopy procedures. A common limitation of existing studies is their reliance on retrospective video clips, which fail to capture the dynamic demands of real-time colonoscopy, where endoscopists must simultaneously navigate the colonoscope and identify lesions on the screen. To address this gap, we established a dataset to analyze changes in endoscopists’ eye movements during the colonoscopy withdrawal phase. Eye-tracking data was collected from graduate students, nurses, senior endoscopists, and novice endoscopists while they reviewed retrospectively recorded colonoscopy withdrawal videos, both with and without computer-aided detection (CADe) assistance. Furthermore, 80 real-time video segments were prospectively collected during endoscopists’ actual colonoscopy withdrawal procedures, comprising 43 segments with CADe assistance and 37 segments without assistance (normal control).https://doi.org/10.1038/s41597-025-04535-6 |
| spellingShingle | Yan Zhu Rui-Jie Yang Pei-Yao Fu Zhen Zhang Yi-Zhe Zhang Quan-Lin Li Shuo Wang Ping-Hong Zhou Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition Scientific Data |
| title | Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition |
| title_full | Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition |
| title_fullStr | Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition |
| title_full_unstemmed | Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition |
| title_short | Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition |
| title_sort | eye tracking dataset of endoscopist ai teaming during colonoscopy retrospective and real time acquisition |
| url | https://doi.org/10.1038/s41597-025-04535-6 |
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