基于心电图的基因型预测,以深度学习为导向,用于高性心肌病:一个试点研究
LaiTe Chen1, GuoSheng Fu2,3, ChenYang Jiang2
1The Affiliated Eye Hospital of Wenzhou Medical University, Wenzhou, P.R. China.
Annals of medicine
|July 19, 2023
概括
一个新的卷积神经网络 (CNN) 工具可以使用心电图 (ECG) 预测高性心肌病 (HCM) 的遗传概率. 这种人工智能模型的表现优于传统的分数,并有助于HCM患者突然心脏死亡风险分层.
科学领域:
- 心脏病学 心脏病学
- 医疗人工智能 医疗人工智能
- 遗传学 是一个遗传学.
背景情况:
- 增高性心肌病 (HCM) 对患者来说带来了重大的心理社会和伦理挑战.
- 在基因检测之前确定遗传概率可以使HCM患者受益.
- 目前对HCM基因型的预测方法存在局限性.
研究的目的:
- 开发一种简单的工具,用于对高性心肌病 (HCM) 患者的基因型预测.
- 评估卷积神经网络 (CNN) 与传统评分系统的性能.
- 探索CNN在HCM突然心脏死亡 (SCD) 风险分层中的实用性.
主要方法:
- 通过使用12导电心电图 (ECG) 数据从124名接受基因检测 (GT) 的HCM患者中开发了CNN.
- 该CNN模型在单独的HCM队列 (n=54) 上进行了外部验证,并与梅奥和多伦多的得分进行了比较.
- 第三个HCM队列 (n=76) 用于根据预测的基因型评估SCD风险 (HCM风险-SCD),并使用Score-CAM进行可视化.
主要成果:
- 总的来说,45% (80/178) 的HCM患者是基因型阳性的.
- 在测试组中,CNN的AUC达到0.89,明显优于梅奥 (0.69) 和多伦多 (0.69) 的得分 (p < 0.001).
- 预测为基因型阳性的患者与预测为基因型阴性的患者相比,HCM风险-SCD得分显著更高 (p < 0.01).
结论:
- 开发的CNN显示了使用心电图数据准确预测HCM患者的基因型的有希望的能力.
- 该模型有效地帮助对高性心肌病中心脏突然死亡的风险分层.
- 肢体线索被确定为CNN通过可视化预测准确度的关键贡献者.
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