Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images.

ABSTRACT: It can be difficult/impossible to fully expand a coronary artery stent in a heavily calcified coronary artery lesion. Under-expanded stents are linked to later complications. Here we used machine/deep learning to analyze calcifications in pre-stent intravascular optical coherence tomography (IVOCT) images and predicted the success of vessel expansion. Pre- and post-stent IVOCT image data were obtained from 110 coronary lesions. Lumen and calcifications in pre-stent images were segmented using deep learning, and lesion features were extracted. We analyzed stent expansion along the lesion, enabling frame, segmental, and whole-lesion analyses. We trained regression models to predict the post-stent lumen area and then computed the stent expansion index (SEI). Best performance (root-mean-square

SUBMITTER: Gharaibeh Y 

PROVIDER: S-EPMC10593923 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature