转移学习用于预测急性心肌梗塞使用心电图
Axel Nyström1,2, Anders Björkelund2, Mattias Ohlsson2,3
1Department of Laboratory Medicine, Lund University, Lund, Sweden.
PLOS digital health
|October 31, 2025
概括
转移学习显著改善了使用心电图 (ECG) 预测急性心肌梗塞 (AMI) 的预测. 对非胸痛心电图的预训练模型提高了在胸痛患者中检测AMI的诊断准确性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 准确和快速识别急性心肌梗塞 (AMI) 在紧急情况下至关重要.
- 电心电图 (ECG) 对于AMI检测至关重要,但手动解释是具有挑战性的.
- 机器学习用于心电图分析需要广泛的,高质量的标记数据,这往往很少.
研究的目的:
- 调查转移学习在改进机器学习模型中的有效性,用于ECG的AMI预测.
- 评估预训练对非胸痛心电图数据的影响,以后用于AMI检测.
- 将转移学习模型与没有预训练的传统模型进行比较.
主要方法:
- 利用来自非胸痛患者84万张心电图的大数据集进行模型预训练 (性别和年龄分类).
- 微调了预先训练的模型,使用来自胸痛患者的44,000张心电图数据集进行AMI预测.
- 在各种最先进的ResNet架构和数据大小中评估性能,与非转移学习方法进行比较.
主要成果:
- 转移学习在AMI预测准确度方面表现出了显著的改善.
- 性能最好的模型实现了AUC从0.79增加到0.85.
- 在不同的ResNet架构和数据集尺度上,改进是一致的.
结论:
- 从非胸痛心电图数据的简单转移学习显著增强了AMI预测模型.
- 这种方法有效地减轻了在开发精确的基于心电图的诊断工具时的数据稀缺问题.
- 转移学习为改善紧急心脏诊断提供了一个有希望的策略.
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