Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound-Based Deep Learning

The aim of this study was to develop pre-procedural intravascular ultrasound (IVUS)-based models for predicting the occurrence of stent underexpansion.

Although post-stenting IVUS has been used to optimize percutaneous coronary intervention, there are no pre-procedural guidelines to estimate the degree of stent expansion and provide preemptive management before stent deployment.

A total of 618 coronary lesions in 618 patients undergoing percutaneous coronary intervention were randomized into training and test sets in a 5:1 ratio. Following the coregistration of pre- and post-stenting IVUS images, the pre-procedural images and clinical information (stent diameter, length, and inflation pressure; balloon diameter; and maximal balloon pressure) were used to develop a regression model using a convolutional neural network to predict post-stenting stent area. To separate the frames with from those without the occurrence of underexpansion (stent area <5.5 mm²), binary classification models (XGBoost) were developed.

Overall, the frequency of stent underexpansion was 15% (5,209 of 34,736 frames). At the frame level, stent areas predicted by the pre-procedural IVUS-based regression model significantly correlated with those measured on post-stenting IVUS (r = 0.802). To predict stent underexpansion, maximal accuracy of 94% (area under the curve = 0.94) was achieved when the convolutional neural network- and mask image-derived features were used for the classification model. At the lesion level, there were significant correlations between predicted and measured minimal stent area (r = 0.832) and between predicted and measured total stent volume (r = 0.958).

Deep-learning algorithms accurately predicted incomplete stent expansion. A data-driven approach may assist clinicians in making treatment decisions to avoid stent underexpansion as a preventable cause of stent failure.