在血压监测器中使用新型机器学习算法检测心房动的诊断准确性
Matthew Janik1, George Raad2, George Nijmeh3
1Cardiology Division, Wilmington Health, Wilmington, North Carolina.
新的Omron血压监测器 (BPMs) 具有基于机器学习的心房动 (AF) 查,显示出高准确度. 这些设备在家庭和临床环境中为早期AF检测提供了有前途的工具.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 早期发现心房动 (AF) 对于预防中风至关重要.
- 带有集成AF查的血压监测器 (BPM) 提供了家庭早期检测的潜力.
- 新的Omron BPM (HEM-7371T1-AZ,HEM-7372T1-AZAZ) 采用机器学习算法来提高AF选的准确性.
研究的目的:
- 在两个Omron BPM模型中评估一种新型AF选功能的性能.
- 在多中心前性临床研究中评估BPM的诊断准确性.
- 为了比较Omron BPM与Microlife BPM和12导电心电图的性能.
主要方法:
- 总共有559名受试者被录取,分为AF (267) 和非AF (292) 队列.
- 使用两个Omron BPM和一个Microlife BPM进行了AF查.
- 同时的12导电心电图被记录并由一个失明的心脏病学家解释,以建立一个参考标准.
主要成果:
- 奥姆龙HEM-7371T1-AZ BPM表现出高灵敏度 (95.1%),特异性 (98.6%) 和精度 (97.0%).
- 欧姆龙的HEM-7372T1-AZAZ BPM产生了同等的性能结果.
- 这些结果与Microlife BPM相比较好,该BPM的灵敏度 (78.5%) 和准确度 (88.4%) 较低.
结论:
- 基于机器学习的AF查的新型Omron BPM显示出出色的诊断性能.
- 该研究支持使用这些Omron BPM用于家庭和专业AF检测.
- 这些设备代表了可访问的心房的查技术的重大进步.
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