使用卷积神经网络预测心脏起器诱导的心肌病,基于心脏起器植入之前的临床发现
Mitsunori Oida1, Takuya Mizutani2, Eriko Hasumi3
1Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 113-8655, Japan.
Scientific reports
|March 23, 2024
概括
一个新的卷积神经网络 (CNN) 模型可以使用植入前临床数据预测起器诱导的心肌病 (PICM). 这种工具有助于识别有风险的患者,使得及时干预能够预防心肌损伤.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 生物医学工程 生物医学工程
背景情况:
- 心脏起器诱导心肌病 (PICM) 是心脏节奏的一个已知的并发症.
- 预测PICM的发展是具有挑战性的,尽管已经确定了风险因素,如右心室高节奏负担和先前存在的左心室功能障碍.
研究的目的:
- 开发和验证一个卷积神经网络 (CNN) 模型来预测PICM发展.
- 利用心脏起器植入前的临床数据进行PICM早期风险分层.
主要方法:
- 一个CNN模型被训练在一个数据集的31个心脏起器前植入变量从165名患者与双室起器.
- 该数据集包括人口统计,临床,心声和实验室发现.
- 用准确度,灵敏度,特异性和曲线下的面积 (AUC) 来评估模型性能.
主要成果:
- 在CNN模型中,准确度为75.8%,灵敏度为55.6%,特异性为83.3%,AUC为0.78.
- 该模型展示了使用植入前临床数据预测PICM发展的能力.
- 在165名患者中,有47名患者在平均1.7年的随访期间发展出PICM.
结论:
- 基于心脏起器植入前临床数据的CNN模型可以准确预测PICM的发展.
- 这种预测模型可以帮助识别PICM高风险患者.
- 早期识别有助于及时进行干预,例如升级到生理节奏,以防止不良结果.
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