人工智能增强心电图模型中的可解释性成本
Konstantinos Patlatzoglou1, Libor Pastika1, Joseph Barker1
1National Heart and Lung Institute, Imperial College London, London, UK.
NPJ digital medicine
|December 5, 2025
概括
本研究介绍了VAE-SCAN,这是可解释的人工智能心电图 (AI-ECG) 模型的新框架. 它量化了内在心电图解释性的性能成本,为可信的临床AI提供了洞察力.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能增强心电图 (AI-ECG) 模型提供高诊断和预后性能,但由于它们的黑子性质,缺乏临床信任.
- 可解释的人工智能 (XAI) 对透明和可信的医疗决策至关重要,超越不可靠的临时后期方法.
研究的目的:
- 开发一种新的框架 (VAE-SCAN),用于使用变量自编码器 (VAE) 进行显式表示学习,以捕获内在可解释的ECG特征.
- 研究AI-ECG模型中的性能-可解释性权衡,专注于可解释的特征表示.
- 模拟ECG特征和临床因素之间的双向关联.
主要方法:
- 使用变化自编码器 (VAE) 进行ECG特征的显式表示学习.
- 开发了VAE-SCAN框架,以建模可解释的,ECG特征和临床因素之间的双向关联.
- 评估了不同ECG表示对跨模型解码性能的影响,可解释性各不相同.
主要成果:
- 证明通过基于VAE的表示学习实现的内在的ECG解释性引入了性能成本.
- 量化了模型性能和ECG特征可解释性水平之间的权衡.
- 确定了这种权衡对AI-ECG技术临床采用的影响.
结论:
- 使用VAE的显式表示学习可以创建更易于解释的AI-ECG模型.
- 该研究量化了与内在心电图解释性相关的性能成本.
- 结果为开发可靠和临床适用的AI-ECG系统提供了指导.
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