发表机构
Computer Science and Engineering, University of Michigan; Michigan Institute for Data & AI in Society, University of Michigan; School of Information, University of Michigan; Medical School, University of Michigan（密歇根大学计算机科学与工程系; 密歇根大学数据与人工智能社会研究所; 密歇根大学信息学院; 密歇根大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SurgGaze利用手术中器械-组织接触点作为真实注视替代物，通过隐式校准将可穿戴眼动追踪误差降低40.6%，优于传统9点显式校准，并在真实手术中验证了其有效性。
AI 中文摘要
准确的注视追踪对于理解外科医生在腹腔镜手术中的视觉注意力和认知过程至关重要，然而，如研究1所示，可穿戴眼动追踪器会产生与真实注视位置系统性相关的大误差。我们提出了SurgGaze，一种利用高置信度手术时刻来校正这些误差的隐式校准方法。基于外科医生在解剖过程中注视点会汇聚在器械-组织接触点（TTCP）附近的证据，SurgGaze将TTCP作为真实注视的替代物来构建训练对。我们在模拟手术室试验和真实手术室案例研究中评估了SurgGaze。在模拟中，SurgGaze将注视估计误差降低了40.6%，显著优于传统的9点显式校准。案例研究表明，这些时刻提供了可靠的训练数据，且校准后的注视在数值误差减少之外，还能改善对外科医生注意力的解读。这些发现表明，结构化的行为信号能够在复杂的真实世界环境中实现注视追踪的隐式校准。
英文摘要
Accurate gaze tracking is essential for understanding surgeons' visual attention and cognitive processes during laparoscopic surgery, yet wearable eye trackers produce large errors systematically correlated with ground-truth gaze locations, as demonstrated in Study 1. We introduce SurgGaze, an implicit calibration method that corrects these errors using high-confidence surgical moments. Building on evidence that surgeons' gaze converges near the tool-tissue contact point (TTCP) during dissection, SurgGaze uses TTCP as a surrogate for true gaze to construct training pairs. We evaluate SurgGaze in a simulated operating room trial and an authentic operating room case study. In simulation, SurgGaze reduced gaze estimation error by 40.6%, significantly outperforming conventional 9-point explicit calibration. The case study showed that these moments provide reliable training data and that calibrated gaze improves interpretation of surgeons' attention beyond numeric error reduction. These findings demonstrate that structured behavioral signals can enable implicit calibration for gaze tracking in complex real-world settings.
