Prospective Deep Learning-based Quantitative Assessment of Coronary Plaque by CT Angiography Compared with Intravascular Ultrasound.
Jagat NarulaThomas D StuckeyGaku Sr NakazawaAmir AhmadiMitsuaki MatsumuraKersten PetersenSaba MirzaNicholas NgSarah MullenMichiel SchaapJonathan LeipsicCampbell RogersCharles A TaylorHarout YacoubHimanshu GuptaHitoshi MatsuoSarah RinehartAkiko MaeharaPublished in: European heart journal. Cardiovascular Imaging (2024)
Artificial intelligence enabled CCTA quantification and characterization of atherosclerosis demonstrated strong agreement with IVUS reference standard measurements. This tool may prove effective for accurate evaluation of coronary atherosclerotic burden and cardiovascular risk assessment.[ClinicalTrails.gov identifier: NCT05138289].
Keyphrases
- artificial intelligence
- deep learning
- coronary artery
- coronary artery disease
- risk assessment
- machine learning
- big data
- convolutional neural network
- magnetic resonance imaging
- cardiovascular disease
- high resolution
- heavy metals
- human health
- aortic stenosis
- heart failure
- risk factors
- left ventricular
- mass spectrometry
- computed tomography
- ejection fraction