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Damian Waląg¹, Mateusz Soliński², Łukasz Kołtowski³

¹Faculty of Physics, Warsaw University of Technology, Koszykowa St. 75, 00-662, Warsaw, Poland.

Physiological measurement

|August 8, 2023

概括

使用卷积神经网络 (CNN) 的自动算法可以准确评估螺旋计曲线质量,提高测试可靠性,特别是在无监督的环境中. 这种人工智能工具有助于专家高效评估大型数据集.

科学领域:

肺功能测试试验肺功能测试
医学成像分析医学成像分析
医疗保健中的人工智能

背景情况:

螺旋测量质量对于精确解释肺功能参数至关重要.
目前的美国胸腔学会和欧洲呼吸学会 (ATS/ERS) 标准要求手动视觉评估螺旋计曲线.
量化标准的自动评估已经存在,但视觉评估仍然是一个瓶.

研究的目的:

开发和评估一个卷积神经网络 (CNN) 算法,用于自动评估螺旋计曲线的质量.
为提供替代手工验证螺旋图的可接受性和可用性的替代方案.
提高螺旋计质量控制的效率和一致性.

主要方法:

使用了符合ATS/ERS定量标准的1998年螺旋图的数据集.
肺科医生标注每一个螺旋图为"确认"或"拒绝"FEV1和FVC.
一个CNN分类算法被开发和优化使用交叉验证在80%的培训和20%的测试分割.

主要成果:

在FEV1 (92.6%,93.1%,90.0%) 和FVC (94.1%,95.6%,88.3%) 方面,CNN算法实现了高精度,灵敏度和特异性.
该算法在分类螺旋计曲线质量方面表现强.
结果表明,该算法具有可靠的自动化质量评估的潜力.

结论:

开发的CNN算法为螺旋计测试质量评估提供了显著的改进.
它特别有利于无监督的螺旋计,可以简化临床试验中的质量控制.
这种自动化工具可以作为一个有价值的辅助,专家审查大规模的螺旋计数据分析.

关键词:

卷积神经网络是一种卷积神经网络.流量-体积曲线的曲线是质量评估质量评估的质量评估.螺旋测量是一种螺旋测量.

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