
Deep learning may be able to predict incomplete stent expansion, new research in JACC: Cardiovascular Interventions suggests.
“Although post-stenting intravascular ultrasound (IVUS) has been used to optimize percutaneous coronary intervention (PCI), there are no pre-procedural guidelines to estimate the degree of stent expansion and provide preemptive management before stent deployment,” the authors noted in their abstract.
The research team, looking to develop pre-procedural IVUS-based models to predict stent underexpansion, examined a total of 618 coronary lesions in 618 patients undergoing PCI were randomly assigned to training and test sets (5:1 ratio). Pre- and post-stenting IVUS images were obtained (along with clinical information such as stent diameter, length, and inflation pressure; balloon diameter; and maximal balloon pressure), and the pre-procedural models used to develop a regression model using a convolution neural network to predict area post-stenting.