Deep learning model DeepNeo predicts neointimal tissue characterization using optical coherence tomography.

ABSTRACT:

Background

Accurate interpretation of optical coherence tomography (OCT) pullbacks is critical for assessing vascular healing after percutaneous coronary intervention (PCI). Manual analysis is time-consuming and subjective, highlighting the need for a fully automated solution.

Methods

In this study, 1148 frames from 92 OCT pullbacks were manually annotated to classify neointima as homogeneous, heterogeneous, neoatherosclerosis, or not analyzable on a quadrant level. Stent and lumen contours were annotated in 305 frames for segmentation of the lumen, stent struts, and neointima. We used these annotations to train a deep learning algorithm called DeepNeo. Performance was further evaluated in an animal model (male New Zealand White Rabbits) of neoatherosclerosis using co-regist

SUBMITTER: Koch V 

PROVIDER: S-EPMC12006410 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature