使用深层卷积神经网络对十二导电心电图进行自动分类:第一个实施研究
Rutger R van de Leur1, Meike T G M van Sleuwen1, Peter-Paul M Zwetsloot1
1Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.
European heart journal. Digital health
|January 24, 2024
概括
一个名为DELTAnet的人工智能 (AI) 算法有效地对非心脏病学部门的心电图 (ECG) 进行选. 这种人工智能工具是安全和准确的,可以优先考虑ECG,通过确保危急病的及时治疗来改善患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 专家对心电图 (ECG) 的解释通常是有限的.
- 基于人工智能 (AI) 的分类算法DELTAnet可以帮助医生优先考虑心电图.
- 人工智能用于心电图分拣的临床实施尚未得到研究.
研究的目的:
- 预期评估DELTAnet人工智能算法用于ECG分类的临床实施和影响.
- 与最终的临床诊断相比,评估DELTAnet在ECG分类方面的表现.
- 调查DELTAnet预测的分拣类与患者临床结果之间的关联.
主要方法:
- 一项前性队列研究,涉及常规心电图的非心脏病科的成年患者.
- 德尔塔网将12导电心电图分为三级类别:正常,异常,非急性,亚急性和急性.
- 绩效与基于最终临床诊断的分组进行了比较;分析了临床结果.
主要成果:
- 分析了1061名患者和ECG.
- 与临床分组相比,DELTAnet表现出高性能,平均一致性统计值为0.96 (95% CI 0.95-0.97).
- 没有错过任何需要干预的关键心电图,急性选的错误阳性是最小的 (2.6%).
结论:
- 这是第一个基于AI的ECG选算法的临床实施前性研究.
- 在非心脏病医院环境中,DELTAnet是有效和安全的临床使用,用于对非心脏病医院环境中的12导电心电图进行分组.
- 人工智能算法支持高效的心电图优先级,有可能改善急性心脏病的治疗时间.
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