使用深度神经网络从心电图中查查加斯病
Carl Jidling1, Daniel Gedon1, Thomas B Schön1
1Department of Information Technology, Uppsala University, Uppsala, Sweden.
PLoS neglected tropical diseases
|July 3, 2023
概括
深度神经网络显示出从心电图 (ECG) 中检测夏加斯病 (ChD) 的前景,有助于早期诊断. 虽然对慢性查加斯心肌病有效,但早期心肌病的表现需要通过更好的数据集进一步改进.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 传染性疾病 传染性疾病
背景情况:
- 查加斯病 (ChD) 影响全球超过600万人,在慢性阶段往往导致严重的心脏并发症.
- 早期检测慢性病对于及时治疗和预防严重后果至关重要,但目前的检测率仍然很低.
- 电心电图 (ECG) 提供了一种非侵入性心脏评估方法,可能对心脏病查有用.
研究的目的:
- 探索深度神经网络 (DNN) 在使用心电图 (ECG) 数据检测夏加斯病 (ChD) 的有效性.
- 开发和验证一个卷积神经网络 (CNN) 模型,用于从12导心电图中识别心脏病.
- 评估模型在区分慢性心脏病,包括慢性查加斯心肌病 (CCC),与对照组的表现.
主要方法:
- 一个卷积神经网络 (CNN) 模型是使用来自巴西患者的200多万个心电图输入 (SaMi-Trop和CODE研究) 开发的.
- 模型的性能在两个独立的外部数据集上进行了评估:REDS-II (631名慢性病患者) 和ELSA-Brasil (13,739名参与者).
- 性能指标包括接收器运行特征曲线下的面积 (AUC-ROC),灵敏度和特异性.
主要成果:
- 该模型在验证数据集上的AUC-ROC值为0.80,在外部验证数据集上的AUC-ROC值为0.68 (REDS-II) 和0.59 (ELSA-巴西).
- 对于ELSA-巴西数据集,在识别CHD时,灵敏度为0.52,特异性为0.77.
- 当专注于患有夏加斯心肌病的患者时,该模型的AUC-ROC改善到0.82 (REDS-II) 和0.77 (ELSA-巴西).
结论:
- 深度神经网络可以从心电图上检测慢性查加斯心肌病 (CCC),但早期的慢性心肌病的表现较弱.
- 该研究强调需要更大,更高质量的数据集,因为在CODE数据集中观察到自我报告标签的局限性.
- 这些发现为改善慢性病的检测和治疗提供了潜力,特别是在患病率高的地区.
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