Risk predicting for acute coronary syndrome based on machine learning model with kinetic plaque features from serial coronary computed tomography angiography.
Yabin WangHaiwei ChenTing SunAng LiShengshu WangJibin ZhangSulei LiZheng ZhangDi ZhuXinjiang WangFeng CaoPublished in: European heart journal. Cardiovascular Imaging (2021)
Dynamic changes of plaque features are highly relative with subsequent ACS events. The machine learning model of integrating these lesion characteristics (e.g. CT-FFR, necrotic core, remodelling index, plaque volume, and calcium) can improve the ability for predicting risks of ACS events.
Keyphrases
- acute coronary syndrome
- machine learning
- coronary artery disease
- percutaneous coronary intervention
- coronary artery
- antiplatelet therapy
- artificial intelligence
- image quality
- computed tomography
- big data
- magnetic resonance imaging
- deep learning
- human health
- positron emission tomography
- aortic valve
- transcatheter aortic valve replacement