English(EN) Video-based detection of cessation of breathing in pre-term infants using machine learning

AI模型利用视频检测婴儿呼吸暂停

作者 PulseAugur 编辑部 · [3 个来源] · 2026-07-06 15:42

研究人员开发了一种机器学习模型，能够使用非接触式视频监控检测早产儿的呼吸暂停。仅摄像头模型达到了76.9%的平衡准确率，证明了该方法的有效性。当与阻抗气动描记法结合时，混合模型将检测准确率提高到90.6%，优于单独使用任一模态，并表明了视频衍生呼吸信息的价值。 AI

影响这项研究展示了人工智能驱动的视频分析在加强关键婴儿监护方面的潜力，可能改善患者预后并减少对接触式传感器的依赖。

排序理由该集群包含一篇详细介绍新型机器学习模型及其在特定任务上性能的学术论文。

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arXiv cs.LG TIER_1 English(EN) · Dineo Serame, Lionel Tarassenko, Mauricio Villarroel · 2026-07-07 04:00

利用机器学习对早产儿呼吸停止进行基于视频的检测

arXiv:2607.05230v1 Announce Type: new Abstract: Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable respiratory monitoring in the neonatal intensive care unit (NICU) remains challeng…
arXiv cs.LG TIER_1 English(EN) · Mauricio Villarroel · 2026-07-06 15:42

利用机器学习对早产儿呼吸停止进行基于视频的检测

Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable respiratory monitoring in the neonatal intensive care unit (NICU) remains challenging because motion artefacts, sensor displacemen…
Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-06 15:42

利用机器学习对早产儿呼吸暂停进行基于视频的检测

Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable respiratory monitoring in the neonatal intensive care unit (NICU) remains challenging because motion artefacts, sensor displacemen…