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深度学习用于自动检测心声学中的

Luís B Elvas^1,2,3, Sara Gomes⁴, João C Ferreira^5,4,6

¹Department of Logistics, Molde University College, Molde, 6410, Norway. luis.m.elvas@himolde.no.

|August 28, 2024

概括

深度学习模型现在可以在心声图像中自动检测大动脉的化. 这一进步为诊断这种普遍且致命的心血管疾病提供了一个无辐射的替代方案.

科学领域:

心脏病学心脏病学
医疗成像医学成像
人工智能的人工智能

背景情况:

心血管疾病是全球主要的死亡原因.
大动脉狭窄,一种严重的心脏病,往往是多年的大动脉结石化.
目前的非侵入性诊断成像,如CT扫描,涉及辐射暴露.

研究的目的:

开发一种自动化方法,使用心声回声学检测大动脉结石化.
探索深度学习 (DL) 在分析心声回声图像对病理性化的潜力.
建立一个可靠的,无辐射的替代方案来诊断大动脉结石化.

主要方法:

使用卷积神经网络 (CNN) 设计了一种完全自动化的检测方法.
该方法涉及两个阶段:用于大动脉位置的物体探测器和用于识别的分类器.
使用精度和回忆指标来评估性能.

主要成果:

物体探测器在定位大动脉门时达到95%的精度和100%的回忆.
分类器在识别化结构时显示了92%的精度和100%的回忆.
开发的CNN模型成功地自动检测了大动脉化在心声回声图中的检测.

结论:

使用心声回声学自动检测大动脉结石化是可行的深度学习.
这种方法提供了一个有希望的,无辐射的诊断工具,用于流行和致命的条件.
在心声回声成像技术的进一步技术发展可以提高诊断能力.

关键词:

大动脉的化.大动脉硬化症 (大动脉硬化症).大动脉狭窄症大动脉狭窄症心脏病是指心脏病的发生.心血管疾病的心血管疾病.卷积神经网络 (CNN) 是一种神经网络.数据驱动工具是数据驱动的工具.深度学习 (DL) 是指深度学习.诊断诊断诊断诊断是一个心声回声扫描 (Echocardiography) 是一种心声回声扫描.图像的分类图像的分类.物体探测器的物体探测器

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