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Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
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基于深度学习的肺高血压查算法的开发和评估,使用数字耳语镜进行查.

Ling Guo1, Nivedita Khobragade1, Spencer Kieu1

  • 1Eko Health Inc. Emeryville CA USA.

Journal of the American Heart Association
|February 3, 2025
PubMed
概括

一种新的深度学习方法使用数字听筒录像 (心声图) 来选肺高血压. 这种非侵入性工具对早期检测和及时治疗这种严重疾病充满希望.

关键词:
深度学习是一种深度学习.数字耳造影仪数字耳造影仪肺高血压是一种肺高血压.

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科学领域:

  • 心脏病学 心脏病学
  • 医疗成像医学成像
  • 人工智能在医学中的应用

背景情况:

  • 肺高血压往往是未被诊断的,原因是临床怀疑较低,查工具的可用性有限,如心声回声学.
  • 早期检测对于及时治疗潜在原因和改善患者预后至关重要.
  • 需要易于使用的查工具来识别肺动脉静脉压升高.

研究的目的:

  • 开发和验证一种基于深度学习的方法,用于使用心电图 (PCG) 检测肺动脉缩压升高.
  • 评估使用数字耳语镜和人工智能用于非侵入性肺高血压查的可行性.

主要方法:

  • 通过使用大约6000个具有已知的肺动脉缩压值的PCG记录和约169,000个未标记的PCG,训练了一个深度卷积网络.
  • 该模型以半监督的方式训练,以检测肺动脉缩压 ≥40 mmHg.
  • 将PCG记录处理成Mel谱图,并使用GradCAM++进行结果可视化.

主要成果:

  • 该模型通过交叉验证实现了0.79的接收机操作员特征曲线下的平均面积.
  • 在测试数据集上,该模型表现出0.71的灵敏度和0.73.7的特异性.
  • 该GradCAM ++技术成功地突出了PCG记录中的生理上相关的段落,表明肺高血压.

结论:

  • 数字耳镜与深度学习算法相结合,为早期肺高血压检测提供了可行的,低成本,非侵入性和可访问的查工具.
  • 这种方法有可能改善肺高血压的早期诊断和管理.