Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths:
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JACC: Cardiovascular Imaging - Abstract Objectives The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the...
JACC: Cardiovascular Imaging - IntroductionCardiac imaging has played an important role in understanding the presence and extent of cardiac involvement in coronavirus disease-2019 (COVID-19) (1,2), as well as...
RT @RonBlankstein: New Insights on COVID-19 and the Heart: https://t.co/wqLynRMnxC
JACC: Cardiovascular Imaging - AbstractThe first-line evaluation of aortic stenosis severity is Doppler echocardiography. However, in up to 40% of patients, resting echocardiographic assessment of aortic stenosis severity...
7/ If Low flow SVI<35 & EF>50% Do #Yescct to quantitate calcium, gated ID which calcium is in valve🆚LVOT 🆚mitral annulus;Don’t use en face view will underestimate calcium,use axial, AV calcification score >1,300 AU in🙋🏻♀️or 2,000 AU🙋♂️is severe #ASechoJC https://t.co/SF6gAPN7sB https://t.co/SF6gAPN7sB
Calling for a new Framingham score: Is machine learning in #CV risk assessment the 🔑 for improved outcome prediction? In a new #JACCIMG study from the #CAC Consortium, a comprehensive ML model was superior to ASCVD risk, CAC & available #YesCCT variables. https://t.co/IUQ3pnwURO https://t.co/IUQ3pnwURO