临床实践中的全自动心声图解读
Jeffrey Zhang1,2, Sravani Gajjala3, Pulkit Agrawal2
1Cardiovascular Research Institute (J.Z., R.C.D.).
Circulation
|October 26, 2018
概括
使用人工智能的自动心脏图像分析准确地解释回声心脏图,使可扩展的患者监测和疾病检测成为可能. 这种计算机视觉管道通过提供可靠的心脏功能和结构测量来支持临床实践.
科学领域:
- 心脏病学
- 人工智能
- 医学成像
背景情况:
- 自动心脏图像解释可以提高临床实践,特别是在初级保健中的连续评估.
- 计算机视觉的进步为完全自动化的心声图分析管道提供了潜力.
研究的目的:
- 使用卷积神经网络开发和评估可扩展的,自动化的回声心电图解释管道.
- 该管道旨在进行视图识别,图像细分,结构/功能量化和疾病检测.
主要方法:
- 在 14,035 个心声回声图上训练了卷积神经网络模型,用于视图识别和心室细分.
- 使用细分输出量化心脏结构和功能 (射出分数,纵向应变).
- 开发了检测高性心肌病,心脏粉样蛋白和肺动脉高血压的模型.
主要成果:
- 准确的自动视图识别 (96%对横侧长轴) 和室内细分.
- 心脏结构测量显示与临床值一致 (例如,体积中位数绝对偏差为15-17%).
- 自动功能测量 (射出分数,应变) 与商业软件一致,并证明了串行监控的实用性.
- 疾病检测模型实现了高性能 (C统计:高缩性心肌病0. 93,心脏粉样蛋白0. 87,肺动脉高血压0. 85).
结论:
- 自动回声图解读管道为存档心脏成像数据的可扩展分析提供了基础.
- 这项技术支持连续患者跟踪和回声心图解释的广泛临床应用.
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