人工智能云平台通过多标签分类改进了从可插入心脏监测器到25种心脏节律模式的心律失常检测
Fabio Quartieri1, Manuel Marina-Breysse2, Raquel Toribio-Fernandez3
1Department of Cardiology, Ospedale S. Maria Nuova, Reggio Emilia, Italy.
Journal of electrocardiology
|July 20, 2023
概括
人工智能 (AI) 通过分析皮下心电图 (sECG) 数据来检测多种心脏模式,增强可插入心脏监测器 (ICM) 功能,显著提高心脏病学家的诊断效率.
科学领域:
- 心脏病学 心脏病学
- 医疗技术 医疗技术 医学技术
- 人工智能的人工智能
背景情况:
- 电心电图 (ECG) 是诊断心脏病的标准.
- 插入式心脏监测器 (ICM) 提供持续的心脏监测,但模式识别有限.
- 对心电图和ICM衍生的皮下心电图 (sECG) 的手动解释对临床医生来说是耗时的.
研究的目的:
- 评估人工智能算法是否可以将ICM心律失常识别扩展到标准的4个模式之外.
- 评估AI识别更广泛心律异常的能力.
主要方法:
- 一项探索性回顾性研究分析了20名ICM患者的sECG数据.
- 人工智能软件与心脏病专家并行分析了sECG原始数据.
- 来自Confirm RxTM ICMs的数据由威廉TM AI进行处理.
主要成果:
- 人工智能从2261个sECG情节中识别了25个心律模式中的7882个事件.
- 实现了88%的全球准确度,对心肌梗塞,停顿和其他异常有很高的敏感性.
- 与心脏病学家相比,AI表现出更高的效率,减少了sECG分类所需的时间.
结论:
- 人工智能可以在没有事先培训的情况下处理ICM sECG数据,从而扩大设备的性能.
- 人工智能显著减少了心脏病学家花在审查心律模式上的时间.
- 人工智能与ICM的整合提供了一种有希望的方法来增强心脏监测和诊断.
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