人工智能在门诊环境中的ECG解释中的使用:一个范围审查
Raghavee Neupane1, Oksana Denis1, Mollie Goudy1
1Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine at Nova Southeastern University, Fort Lauderdale, USA.
Cureus
|November 10, 2025
概括
人工智能 (AI),特别是深度学习,在门诊环境中提高了心电图 (ECG) 解释的准确性. 这些人工智能工具有望改善心血管诊断,特别是在服务不足的地区,但需要进一步验证.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 心血管疾病是全球主要的死亡原因.
- 电心电图 (ECG) 对于早期检测至关重要,但解释起来很复杂,导致门诊环境中的错误.
- 人工智能的有效性,包括像CNN这样的深度学习模型,在门诊ECG解释中尚未得到充分证实.
研究的目的:
- 对使用深度学习算法在门诊环境中的ECG解释进行范围审查.
- 总结AI在非医院心电图分析中的诊断准确性和临床实用性的发现.
- 确定AI在门诊心血管诊断中的局限性和未来研究需求.
主要方法:
- 通过EMBASE,Ovid MEDLINE和科学网络进行系统的文献搜索.
- 在门诊护理中包括基于AI的ECG解释研究;排除ED研究,病例报告,评论和理论模型.
- 使用Joanna Briggs Institute工具提取研究设计,人工智能方法,临床结果和质量评估的数据.
主要成果:
- 人工智能辅助的心电图解读,特别是深度学习,提高了诊断准确度,并使得更早地检测心脏病状况.
- 人工智能工具在资源有限的门诊机构中显示出巨大的潜力,减少了错误诊断和专家负担.
- 确定的局限性包括小/均的样本大小,潜在的过度诊断,数据标准化问题和缺乏外部验证.
结论:
- 人工智能在门诊心电图解释中的整合为改善心血管诊断提供了巨大的希望,特别是在服务不足的地区.
- 进一步的研究必须集中在更大,更多样化的数据集和强大的临床验证上,以确保可概括性和可靠性.
- 心脏病专家的监督对于确保AI工具在现实应用中的临床安全性和有效性至关重要.
关键词:
检测心律失常 检测心律失常人工智能的人工智能是人工智能.心脏氨基粉症的发生.心血管疾病心血管疾病卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.诊断的准确性 诊断的准确性电心电图 (ECG) 是一种心电图.机器学习是机器学习.门诊护理 门诊护理 门诊护理更多相关视频
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