Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images
Yazan Gharaibeh, Juhwan Lee, Vladislav N. Zimin, Chaitanya Kolluru, Luís Augusto Palma Dallan, Gabriel Tensol Rodrigues Pereira, Armando Vergara-Martel, Justin N. Kim, Ammar Hoori, Pengfei Dong, Peshala Thibbotuwawa Gamage, Linxia Gu, Hiram G. Bezerra, Sadeer Al‐Kindi, David L. Wilson
Abstract
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-error = 0.04 ± 0.02 mm 2 , r = 0.94 ± 0.04, p < 0.0001) was achieved when we used features from both lumen and calcification to train a Gaussian regression model for segmental analysis of 31 frames in length. Stents with minimum SEI > 80% were classified as “well-expanded;” others were “under-expanded.” Under-expansion classification results (e.g., AUC = 0.85 ± 0.02) were significantly improved over a previous, simple calculation, as well as other machine learning solutions. Promising results suggest that such methods can identify lesions at risk of under-expansion that would be candidates for intervention lesion preparation (e.g., atherectomy).