Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decision making; thus, understanding where a provider is looking during neonatal resuscitations could inform provider training, enhance real-time decision support, and improve the design of delivery rooms and neonatal intensive care units (NICUs). Current approaches to quantifying neonatal providers' gaze rely on manual coding or simulations, which limit scalability and utility. Here, we introduce an automated, real-time, deep learning approach capable of decoding provider gaze into semantic classes directly from first-person point-of-view videos recorded during live resuscitations. Combining state-of-the-art, real-time segmentation with vision-language models (CLIP), our low-shot pipeline attains 91\% classification accuracy in identifying gaze targets without training. Upon fine-tuning, the performance of our gaze-guided vision transformer exceeds 98\% accuracy in gaze classification, approaching human-level precision. This system, capable of real-time inference, enables objective quantification of provider attention dynamics during live neonatal resuscitation. Our approach offers a scalable solution that seamlessly integrates with existing infrastructure for data-scarce gaze analysis, thereby offering new opportunities for understanding and refining clinical decision making.

通过第一人称视角视频的实时深度学习方法，本研究展示了进行新生儿复苏期间提供者凝视定位的自动化解析，为提高提供者培训、实时决策支持以及优化产房和新生儿重症监护病房设计提供了可能。该方法能够客观地量化提供者在新生儿复苏中的注意力变化，为数据稀缺的凝视分析提供了可扩展的解决方案，从而为改进临床决策提供了新的机会。

视觉语言模型用于解码新生儿复苏期间的医护人员关注点