一个基于深度学习的管道,用于大规模心声回声学数据策划和测量
Jieyu Hu1, Sindre Hellum Olaisen1, David Pasdeloup1
1Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway.
European heart journal. Digital health
|November 21, 2025
概括
自动化数据策划和对心声回声图的深度学习分析使得快速,一致的心脏测量. 这种方法提高了大型回声数据库的研究和诊断的实用性,克服了手动分析的局限性.
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:
- 心声图数据对于心脏研究来说是一个有价值但未被充分利用的资源.
- 手动测量是耗时的,容易变化.
- 数据策划和质量控制对于真实世界的数据分析至关重要.
研究的目的:
- 开发一条自动化管道,用于大型心声回声图数据库的数据处理.
- 使用深度学习评估左心室喷射率 (LVEF) 和左心房体积指数 (LAVI) 的自动测量.
主要方法:
- 开发了一种自动化管道,用于策划14326次心声回声图检查.
- 应用深度学习用于LVEF和LAVI的自动测量.
- 使用布兰德-阿尔特曼分析对手工测量进行验证的自动测量.
主要成果:
- 自动化LVEF测量显示偏差 [SD]为-1.8% (7.6%).
- 自动化LAVI测量显示偏差[SD]为3.3毫升/平方米 (8.1毫升/平方米).
- 该管道在没有临床数据的大型数据集中实现了79%的高可信度测量.
结论:
- 基于深度学习的自动测量显示出在大型心声回声图数据库中数据挖掘的潜力.
- 自动化管道促进了快速,一致和可靠的心脏测量.
- 这种方法可以推进心脏成像研究和诊断.
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