使用人工智能从心电图检测结构性心脏病
Timothy J Poterucha1, Linyuan Jing2, Ramon Pimentel Ricart1
1Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
Nature
|July 16, 2025
概括
一个新的AI模型EchoNext可以使用心律数据检测多种结构性心脏病. 这种深度学习工具具有很高的准确性,
科学领域:
- 心脏病学
- 人工智能
- 医学成像
背景情况:
- 早期发现结构性心脏病对于改善患者的治疗结果至关重要.
- 目前的查方法,如心声扫描,由于成本和可访问性而受到限制.
- 以前用于心脏病检测的人工智能模型通常在有限的人群或特定条件上进行训练.
研究的目的:
- 介绍一个深度学习模型EchoNext,
- 评估EchoNext的诊断准确性和通用性.
- 评估人工智能在扩大心脏病查方面的潜力.
主要方法:
- 开发了一个深度学习模型EchoNext,
- 在内部和外部验证模型的性能.
- 在没有先前心脏成像的患者中进行了前性临床试验.
- 与心脏病专家进行了对比.
主要成果:
- 在不同的人群和护理环境中,EchoNext的诊断准确度很高.
- 模型在受控评估中表现优于心脏病学家.
- 预期试验显示成功识别了以前未被诊断的心脏病.
- 在不同种族和/或族群中观察到一致的表现.
结论:
- 像EchoNext这样的深度学习模型显示出改善结构性心脏病查的巨大潜力.
- 人工智能可以帮助克服传统成像工具在广泛查方面的局限性.
- 模型重量和数据的公开发布支持对心血管健康人工智能的进一步研究和透明度.
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