Abstract
Objective: Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania, and the aim is to analyze the treatment and its effect on the newborn outcome. An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes. Methods: We propose a two-step deep neural network system, ORAA-net, utilizing low-quality video recordings of resuscitation episodes to do activity recognition during newborn resuscitation. The first step is to detect and track relevant objects using Convolutional Neural Networks (CNN) and post-processing, and the second step is to analyze the proposed activity regions from step 1 to do activity recognition using 3D CNNs. Results: The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 %, a mean recall of 77,64 %, and a mean accuracy of 92.40 %. Moreover, the accuracy of the estimated number of Health Care Providers (HCPs) present during the resuscitation episodes was 68.32 %. Conclusion: The results indicate that the proposed CNN-based two-step ORAAnet could be used for object detection and activity recognition in noisy low-quality newborn resuscitation videos. Significance: A thorough analysis of the effect the different resuscitation activities have on the newborn outcome could potentially allow us to optimize treatment guidelines, training, debriefing, and local quality improvement in newborn resuscitation.
Abstract (translated)
目标:出生窒息是Neonatal 死亡的主要原因之一。生存的关键是进行即时和持续的高质量新生儿复苏。一个关键步骤是在坦桑尼亚哈杰多收集的新生儿复苏期间记录的信号数据集,包括视频,旨在分析治疗及其对新生儿结果的影响。一个重要步骤是在复苏期间生成相关的复苏活动时间线,包括通气、刺激、吸氧和吸痰等。方法:我们提议使用两个步骤的深度神经网络系统——ORAA-net,利用低质量的复苏期间视频录制进行活动识别。第一个步骤是使用卷积神经网络(CNN)和后处理检测和跟踪相关物体。第二个步骤是使用3DCNNs从第一个步骤中分析提议的活动区域。结果:系统在新生儿复苏期间识别了未暴露的活动、刺激、通气和吸痰,其平均精度为77.67%,平均召回率为77.64%,平均准确度为92.40%。此外,估计在复苏期间存在的医疗服务提供者数量的准确性为68.32%。结论:结果显示,提议的基于CNN的两步ORAA-net可以在嘈杂的低质量新生儿复苏视频中进行物体检测和活动识别。 significance:全面分析不同复苏活动对新生儿结果的影响可能使我们优化治疗指南、培训、汇报和当地质量改进新生儿复苏。
URL
https://arxiv.org/abs/2303.07789
