人工智能辅助的心声回声监测在外体膜氧化的儿科患者
Weiling Chen1, Jinhui Wu2, Zhenxuan Zhang2
1Department of Ultrasonography, Shenzhen Children's Hospital, Shenzhen, China.
Frontiers in cardiovascular medicine
|December 26, 2024
概括
人工智能 (AI) 提供了一种快速且可重复的方法,用于评估在接受体外膜氧化 (ECMO) 的儿科患者的心脏功能. 这种以人工智能为导向的方法显示了与专家评估的高度一致性,提高了ECMO处理期间的定量准确性.
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:
- 皮肤外体膜氧化 (ECMO) 对患有严重心脏病的儿科患者至关重要.
- 传统的左心室喷射率 (EF) 评估通过心声回声是耗时和主观的.
- 人工智能 (AI) 为ECMO患者的快速,可重现的心脏功能测量提供了潜力.
研究的目的:
- 评估人工智能的适用性,用于监测心脏功能使用心声回声学在儿科ECMO患者.
- 将基于人工智能的EF测量与初级医生和专家执行的传统手动方法进行比较.
主要方法:
- 在ECMO (2017-2021) 上对29名儿科患者进行了回顾性研究.
- 使用手动追踪 (初级EF,专家EF) 和支持人工智能的软件 (自动化EF) 分析心声图.
- 在不同的时间点上对EF测量进行比较:ECMO前,管时,ECMO支持,断奶和断奶后.
主要成果:
- 自动化EF和专家EF之间没有显著差异 (P > 0.05).
- 在初级EF和自动EF和专家EF之间观察到统计学上显著的差异 (P < 0.05).
- 与初级评估 (0.932) 相比,专家手动跟踪的自动化EF具有高的跨组相关系数 (ICC) (0.983).
结论:
- 人工智能自动化EF测量对于儿科ECMO心声学是可行的和可靠的.
- 人工智能分析显示出与临床专家评估的良好一致性和可重复性.
- 自动化方法通过提高定量评估准确性来支持ECMO处理.
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