使用深度神经网络进行异常血压的非接触式遥感:一种用于高血压查的新方法
Zeye Liu1,2,3,4, Hang Li1,2,3,4, Wenchao Li5
1Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Quantitative imaging in medicine and surgery
|December 18, 2023
概括
这项研究引入了一种新的深度学习方法,用于使用非接触式红外热谱检测高血压 (HBP). 这种创新技术显示出在公共场合和家庭环境中广泛开展高血压查的潜力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 高血压是一个日益增长的全球健康挑战,需要早期检测和管理.
- 远程温度计为生理监测提供了一种非侵入性的方法.
- 深度学习模型可以分析医疗数据中的复杂模式.
研究的目的:
- 开发和验证一种深度学习 (DL) 模型,用于使用红外热谱预测异常高血压 (HBP).
- 评估远程温度计在非接触式高血压查中的有效性.
- 评估PanycNet DL模型在识别高血压方面的性能.
主要方法:
- 一项横截面研究使用了252名志愿者的红外热图.
- 根据热成像数据,PanycNet深度学习模型被训练来预测异常血压 (BP).
- 使用标准监测器测量血压,异常水平定义为静脉血压≥140 mmHg和/或静脉血压≥90 mmHg.
主要成果:
- DL模型从上肢体温度图 (AUC=0.983) 中获得了对HBP的高预测准确度.
- 头部区域表现出最强的预测能力,AUC为0.868.8.
- 这项研究包括252名参与者,其中29.20%的人表现出异常高血压.
结论:
- 这代表了第一个非接触性高血压查技术,使用易于获得的设备和数据.
- 该方法对公共区域的大规模人口查有希望.
- 该技术适用于方便的家庭血压监测.
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