是信号还是噪音? 评估常用的归因方法来解释心电图分类中的深度神经网络
Bauke K O Arends1, Wouter A C van Amsterdam2, Pim van der Harst1
1Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
European heart journal. Digital health
|March 16, 2026
概括
深度神经网络心电图分析的归因方法显示出有限的可靠性和不稳定性. 建议在临床使用时谨慎使用,优先考虑验证或固有的可解释模型.
科学领域:
- 医疗保健中的人工智能
- 医学成像和诊断 医学成像和诊断
- 计算心脏病学 计算心脏病学
背景情况:
- 深度神经网络 (DNN) 越来越多地用于心电图 (ECG) 分类.
- 归因方法对于在临床环境中解释DNN预测至关重要.
- 评估这些可解释性技术的可靠性对于安全部署至关重要.
研究的目的:
- 评估基于DNN的ECG分类的12种归因方法的临床实用性.
- 评估这些方法产生的解释的清晰度和准确性.
- 确定医学AI中归因方法的可靠性和稳定性.
主要方法:
- 对卷积神经网络模型应用的12种归因方法的分析.
- 在9个诊断类别中利用了873,710个中位节拍心电图的大数据集.
- 通过方法间相似性,自我一致性,重量依赖性和特征识别来评估性能.
主要成果:
- 深度学习模型实现了高诊断性能 (AUC > 0.95).
- 归因方法表现出低的方法间相关性和高的可变性.
- 观察到适度的自我一致性 (平均相关性为0.41-0.65) 和与模型重量变化的不稳定性.
结论:
- 归因方法显示出有限的可靠性和不稳定性,限制了它们在关键医疗保健应用中的使用.
- 对于临床决策支持,建议进行特定任务的验证和内在可解释的模型.
- 建议谨慎应用当前的归因技术,并补充健康检查.
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