Magnetocardiography-based coronary artery disease severity assessment and localization using spatiotemporal features.

Xiaole HanJiaojiao PangDong XuRuizhe WangFei XieYanfei YangJiguang SunYu LiRuochuan LiXiaofei YinYansong XuJiaxin FanYiming DongXiaohui WuXiaoyun YangDexin YuDawei WangYang GaoMin XiangFeng XuJinji SunYuguo ChenXiaolin Ning

Published in: Physiological measurement (2023)

The developed method enables the implementation of an automated system for severity assessment and localization of CAD. The amplitude and correlation features were key factors for severity assessment and localization. The proposed machine learning method can provide clinicians with an automatic and accurate diagnostic tool for interpreting MCG data related to CAD, possibly promoting clinical acceptance.

Keyphrases

pulmonary arterial hypertension
pulmonary artery
machine learning
coronary artery
coronary artery disease
healthcare
primary care
deep learning
high resolution
artificial intelligence
quality improvement
mass spectrometry
data analysis