Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition - Yan Zhu; Rui-Jie Yang; Pei-Yao Fu; Zhen Zhang; Yi-Zhe Zhang; Quan-Lin Li; Shuo Wang; Ping-Hong Zhou (Scientific Data)
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).