08:30 - 10:00 | Wed 24 Jul | M4 - Level 3 | WeA11
Machine learning algorithms enable automatic analysis of multidimensional data from medical imaging examinations and other clinical information. These methods can be combined with atlas-based analysis of heart geometry and function to give morphometric indices which are optimally associated with clinical factors. We describe methods which can be used to characterize patients with heart failure according to a rich set of morphological features which may give insight into the underlying pathological processes.
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