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DTSTAMP:20250822T115807Z
LOCATION:Campussaal - Plenary Room
DTSTART;TZID=Europe/Stockholm:20250617T173000
DTEND;TZID=Europe/Stockholm:20250617T183000
UID:submissions.pasc-conference.org_PASC25_sess113_key102@linklings.com
SUMMARY:IK03 - Machine Learning Assessment of Coronary Artery Disease Usin
 g X-Ray Angiography
DESCRIPTION:Alberto Figueroa (University of Michigan)\n\nThe diagnosis of 
 Coronary Artery Disease (CAD) and Coronary Microvascular Dysfunction (CMD)
  relies on different tests. Percutaneous approaches have made it possible 
 to assess epicardial and microcirculation disease states in a single proce
 dure, although this approach is rarely utilized due to its invasiveness an
 d complexity. Conversely, there are over 4 million cardiac catheterization
  procedures performed yearly in the USA and Europe alone, making it one of
  the most used diagnostic procedures, although with a recognized low diagn
 ostic yield.<br>Given the high number of catheterization procedures and it
 s low diagnostic yield, there is a pressing need to develop methods to ext
 ract diagnostic information for both CAD and CMD from coronary angiography
  data. The Computational Vascular Biomechanics Lab at the University of Mi
 chigan has developed data-driven computational models and machine learning
  tools for anatomical and functional characterization of CAD and CMD using
  angiography. An overview of these tools and their various applications wi
 ll be presented in the talk.\n\nSession Chair: Peter Vincent (Imperial Col
 lege London)\n\n
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