Assessing aortic morphology post aortic valve replacement using unsupervised hierarchical clustering and statistical shape modelling

@Article {
title = {Assessing aortic morphology post aortic valve replacement using unsupervised hierarchical clustering and statistical shape modelling},
journal = {European Heart Journal},
pages = {ehae6663629},
volume = {45},
keywds = {
aorta heart valve prosthesis computed tomography descending aorta ascending aorta aortic valve replacement ross procedure decision making languages reconstructive surgical procedures software surgical procedures, operative templates aortic valve dis
}
abstract = {

There are different surgical procedures used to treat aortic valve disease such as aortic valve replacement (AVR), the Ozaki procedure, the Ross procedure, and the valve-sparing procedure. There may be postoperative side effects associated with aortic morphology. Computational analyses that assess morphology are thus necessary in such scenario. Statistical shape modelling is used to assess three-dimensional morphology, discover unique shape features, create mean shapes, and create shape modes that display morphological variability. Hierarchical cluster analysis is a machine learning method used to classify a population into subgroups. Both of these statistical methods were applied to aortic valve replacement patients to evaluate morphological variability after aortic valve replacement (AVR) surgery.

}
doi = {10.1093/eurheartj/ehae666.3629},
date = {2024-10-28},
}