非遗传因素决定了深度学习,确定了黑人和白人健康受试者之间的ECG差异
Sandeep Chandra Bollepalli1, Eric M Isselbacher2, Jagmeet P Singh3
1Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA USA.
NPJ cardiovascular health
|October 13, 2025
概括
人工智能模型可以从健康的黑人和白人身上检测出基于种族的心电图 (ECG) 差异. 这些ECG中与种族相关的模式不是遗传的,并且出现在出生后.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 人工智能 (AI) 模型从医疗数据中分析患者种族,引发了公平和公平的担忧.
- 了解生理数据中的基于种族的差异对于公平的医疗保健至关重要.
- 电心电图 (ECG) 是广泛使用的诊断工具,有可能出现与种族相关的微妙变化.
研究的目的:
- 调查机器学习模型是否可以从健康的黑人和白人个体中识别基于种族的ECG差异.
- 探索这些与种族相关的ECG差异的起源和特征.
- 评估性别和社会经济地位等因素对模型性能的影响.
主要方法:
- 分析了来自多个机构的176万名受试者的大约1000万个心电图痕迹.
- 开发和优化基于卷积神经网络 (CNN) 的分类器,用于种族检测.
- 评估各种CNN配置,包括网络深度,融合策略和输入格式 (中位数).
主要成果:
- 性能最好的CNN模型在种族检测方面达到86.17 (±0.34) 的曲线下面面积 (AUC).
- 模型的性能在两性之间一致 (AUC≈86%).
- 解释性分析确定QRS复合体是种族相关模式的关键贡献者,这些模式出现在出生后.
结论:
- 在健康个体的心电图中存在非遗传性,与种族相关的模式.
- 机器学习模型可以检测到这些模式,强调需要仔细考虑AI应用中的公平和公平.
- 需要进一步的研究来了解这些心电图差异的临床影响和起源.
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