Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease.
Hong Yan QiaoChun Xiang TangU Joseph SchoepfChristian TescheRichard R BayerDante A GiovagnoliH Todd HudsonChang Sheng ZhouJing YanMeng Jie LuFan ZhouGuang Ming LuJian Wei JiangLong Jiang ZhangPublished in: European radiology (2020)
• ML-based FFRCT shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA. • FFRCT noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD. • Reserving ICA and revascularization for vessels with positive FFRCT may reduce the normalcy rate of ICA and improve its efficiency.
Keyphrases
- coronary artery disease
- percutaneous coronary intervention
- coronary artery bypass grafting
- machine learning
- decision making
- coronary artery
- cardiovascular events
- aortic stenosis
- artificial intelligence
- type diabetes
- early onset
- heart failure
- computed tomography
- magnetic resonance
- magnetic resonance imaging
- deep learning
- image quality