在急性冠状动脉综合征中用于心电图解释的人工神经网络:范围审查
Andrew J Bishop1, Ziad Nehme2, Shane Nanayakkara3
1Ambulance Victoria, Doncaster, Victoria, Australia; Department of Paramedicine, Monash University, Frankston, Victoria, Australia.
The American journal of emergency medicine
|June 27, 2024
概括
人工神经网络 (ANN) 与人类医生相比,在解释急性冠状动脉综合征 (ACS) 的心电图 (ECG) 中显示出更高的准确性. 这项技术为更快,更可靠地诊断急性冠状动脉封闭 (ACO) 提供了一个有前途的解决方案.
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:
- 电心电图 (ECG) 在急诊室 (ED) 中对于诊断急性冠状动脉综合征 (ACS) 至关重要.
- 传统的心电图标准对急性冠状动脉阻塞 (ACO) 的敏感性有局限性,并存在评分器间可靠性问题.
- 新兴的ECG功能和人工神经网络 (ANN) 为改善早期ACO诊断提供了潜力.
研究的目的:
- 系统地审查有关使用ANN用于ED中ACS患者的ECG解释的文献.
- 与人类解释器相比,评估ANN的诊断准确性和可靠性.
主要方法:
- 在主要数据库 (Ovid MEDLINE,CINAHL,EMBASE,Cochrane,PubMed,Scopus) 进行了全面的搜索,截至2023年12月8日.
- 灰色文献和参考清单也进行了搜索.
- 包括报告ED患者ECG解读ANN的研究在内.
主要成果:
- 在最初的244篇文章中,由于异质性,14篇被纳入分析.
- 在各种结果中,ANN模型表现出比医生解释器更高的准确性,无论医生的经验如何.
- 通过ANN,可以克服传统ECG解释的局限性.
结论:
- 对疑似ACS的ECG的ANN解释是准确的,并且可能优于人类解释和现有的算法.
- 在急性冠状动脉阻塞 (ACO) 诊断中,ANNs提供了一种一致且可靠的ECG分析方法.
- 未来的研究应该专注于ANN用于ACO中的ECG解释,以促进及时的再输液疗法.
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