用热成像融合视频与图像，精准识别新生儿出生时间。
Two-Stream Thermal Imaging Fusion for Enhanced Time of Birth Detection in Neonatal Care
- 双流结构融合静态图像与动态视频信息
- 95.7%精度、84.8%召回率，误差中位数仅2秒
- 适合新生儿急救场景的自动化时间记录
约10%新生儿需辅助呼吸，5%需通气支持。准确记录出生时间（ToB）对优化新生儿护理至关重要，及时干预可保障有效复苏。然而，当前临床记录方式多依赖人工，易出错。本文提出一种新型双流热成像融合系统，结合图像与视频分析，从产房和手术室的热成像数据中精确检测出生时刻。通过整合静态与动态特征流，捕捉更丰富的时空变化信息，显著提升检测鲁棒性。实验表明，该方法优于单流模型。系统在短视频片段上实现95.7%精度、84.8%召回率；经得分聚合模块处理后，在所有测试案例中均成功定位出生时间，中位绝对误差为2秒，平均绝对偏差为4.5秒，接近人工标注结果。
原文摘要 · Abstract (English)
Around 10% of newborns require some help to initiate breathing, and 5\% need ventilation assistance. Accurate Time of Birth (ToB) documentation is essential for optimizing neonatal care, as timely interventions are vital for proper resuscitation. However, current clinical methods for recording ToB often rely on manual processes, which can be prone to inaccuracies. In this study, we present a novel two-stream fusion system that combines the power of image and video analysis to accurately detect the ToB from thermal recordings in the delivery room and operating theater. By integrating static and dynamic streams, our approach captures richer birth-related spatiotemporal features, leading to more robust and precise ToB estimation. We demonstrate that this synergy between data modalities enhances performance over single-stream approaches. Our system achieves 95.7% precision and 84.8% recall in detecting birth within short video clips. Additionally, with the help of a score aggregation module, it successfully identifies ToB in 100% of test cases, with a median absolute error of 2 seconds and an absolute mean deviation of 4.5 seconds compared to manual annotations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。