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-registered histopathology images as the gold standard.
Results: DeepNeo demonstrates a strong classification performance for neointimal tissue, achieving an overall accuracy of 75%, which is comparable to manual classification accuracy by two clinical experts (75% and 71%). In the animal model of neoatherosclerosis, DeepNeo achieves an accuracy of 87% when compared with histopathological findings. For segmentation tasks in human pullbacks, the algorithm shows strong performance with mean Dice overlap scores of 0.99 for the lumen, 0.66 for stent struts, and 0.86 for neointima.
Conclusions: To the best of our knowledge, DeepNeo is the first deep learning algorithm enabling fully automated segmentation and classification of neointimal tissue with performance comparable to human experts. It could standardize vascular healing assessments after PCI, support therapeutic decisions, and improve risk detection for cardiac events.
Optical coherence tomography (OCT) is an imaging test used to detect blood vessel healing after a minimally invasive procedure that widens blocked heart arteries. However, this analysis is performed manually, which is time-consuming and can lead to inconsistencies in diagnosis between clinicians. In this study, we developed a computer-aided tool called DeepNeo that can analyze OCT images of blood vessels and detect vascular healing automatically. We trained the computer-aided technology using manually annotated OCT images and tested its performance on both human and animal data. DeepNeo performed to a similar degree as clinicians. Our findings suggest that DeepNeo can standardize and automate OCT image analysis, potentially improving the efficiency of vascular healing assessments and aiding clinical decision-making to reduce the risk of future cardiac events.
© 2025. The Author(s).