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.

翻译：约10%的新生儿需要辅助启动呼吸，5%需要通气支持。准确的出生时间记录对于优化新生儿护理至关重要，因为及时干预对有效复苏至关重要。然而，当前记录出生时间的临床方法通常依赖人工流程，容易产生误差。本研究提出了一种新颖的双流融合系统，结合图像与视频分析的优势，从产房和手术室的热成像记录中精确检测出生时间。通过整合静态与动态流，我们的方法捕获了更丰富的出生相关时空特征，从而实现更稳健、更精确的出生时间估计。我们证明这种数据模态间的协同作用相比单流方法能提升性能。我们的系统在短视频片段中检测出生事件的精确率达到95.7%，召回率为84.8%。此外，借助分数聚合模块，系统在100%的测试案例中成功识别出出生时间，与人工标注相比中位绝对误差为2秒，绝对平均偏差为4.5秒。