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自动图像转录用于使用移动卫生技术进行围产期血压监测
Nasim Katebi1,2, Whitney Bremer1, Tony Nguyen1
1Department of Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America.
PLOS digital health
|October 2, 2024
概括
本研究介绍了用于血压 (BP) 设备的自动图像转录方法,改进了远程监控的数据传输,特别是在低识字人口中. 深度学习模型准确地转录了BP读数,有助于怀孕和产后护理.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 自测血压 (BP) 监测在将数据传输到临床记录方面面临挑战,特别是在低识字人口中.
- 将振荡计设备的数据集成到电子健康记录中,需要强大的方法来准确地读取转录.
- 移动健康 (mHealth) 计划需要有效的方式来收集和利用来自不同人群的BP数据,包括那些资源较少的环境.
研究的目的:
- 开发和验证一种自动化图像转录技术,用于从振荡计装置读取血压.
- 提高自测血压数据的可访问性和可用性,用于临床监测,特别是在怀孕和产后.
- 促进BP数据与医疗健康记录的整合,以改善在低识字环境中的患者管理.
主要方法:
- 开发了一个深度学习模型,将LCD本地化的You Only Look Once (YOLO) 和数字识别的卷积神经网络结合起来.
- 包括色彩校正和值在内的图像处理技术被应用以减轻反射和文物.
- 该模型在危地马拉和格鲁吉亚的mHealth研究数据集上进行了训练和测试,涉及孕妇和产后妇女.
主要成果:
- 自动化图像转录模型的平均绝对误差 (MAE) 远低于FDA推的5mmHg.
- 与没有转移学习的模型相比,具有转移学习的设备独立模型和设备特定模型表现出优异的性能.
- 缩和腹缩血压的特定MAE值为1.2和0.8mmHg (格鲁吉亚IMPROVE) 和0.9和0.5mmHg (危地马拉第二组).
结论:
- 拟议的自动化图像转录模型是有效的,适合一般使用准确的BP设备.
- 这项技术可以显著改善远程BP监测和数据集成,特别有利于低识字和低资源人口.
- 该方法支持通过可访问的mHealth数据加强怀孕期间和产后高血压的管理.
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This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
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Several factors...
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01:17Measurement of Blood Pressure
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