Enhancing percutaneous coronary intervention using TriVOCTNet: a multi-task deep learning model for comprehensive intravascular optical coherence tomography analysis.

Abstract

Neointimal coverage and stent apposition, as assessed from intravascular optical coherence tomography (IVOCT) images, are crucial for optimizing percutaneous coronary intervention (PCI). Existing state-of-the-art computer algorithms designed to automate this analysis often treat lumen and stent segmentations as separate target entities, applicable only to a single stent type and overlook automation of preselecting which pullback segments need segmentation, thus limit their practicality. This study aimed for an algorithm capable of intelligently handling the entire IVOCT pullback across different phases of PCI and clinical scenarios, including the presence and coexistence of metal and bioresorbable vascular scaffold (BVS), stent types. We propose a multi-task deep learning model, named TriVOCTNet, that automates image classification/selection, lumen segmentation and stent struts segmentation within a single network by integrating classification, regression and pixel-level segmentation models. This approach allowed a single-network, single-pass implementation with all tasks parallelized for speed and convenience. A joint loss function was specifically designed to optimize each task in situations where each task may or may not be present. Evaluation on 4,746 images achieved classification accuracies of 0.999, 0.997, and 0.998 for lumen, BVS, and metal stent features, respectively. The lumen segmentation performance showed a Euclidean distance error of 21.72 μm and Dice's coefficient of 0.985. For BVS struts segmentation, the Dice's coefficient was 0.896, and for metal stent struts segmentation, the precision was 0.895 and sensitivity was 0.868. TriVOCTNet highlights its clinical potential due to its fast and accurate results, and simplicity in handling all tasks and scenarios through a single system.