Plaque phenotyping using deep learning in optical coherence tomography imaging

Abstract Background Artificial intelligence (AI) techniques have emerged as powerful tools in various computer vision tasks, particularly in classification, detection, and segmentation. Purpose This study aimed to develop a deep learning model for categorizing optical coherence tomography (OCT) frames into different plaque phenotypes, including thin-cap fibroatheroma (TCFA) and fibroatheroma. Methods A prospective multicenter observational study enrolled 168 patients who underwent percutaneous coronary intervention (PCI) and received OCT imaging for non-target lesions with angiographically estimated diameter stenosis greater than 30% (177 vessels). Among them, 129 patients (135 vessels) underwent follow-up OCT imaging at 1 year. A total of 45,072 OCT frames were randomly divided into training and validation sets in a 4:1 ratio. An EfficientNet-B3 convolutional neural network model was developed to classify OCT frames into TCFA, thick-cap fibroatheroma, non-fibroatheroma, adaptive intimal thickening, and normal vessel categories. Results A confusion matrix comparing manually assessed plaque phenotypes with AI-predicted phenotypes was presented in Table. In the validation datasets (9,015 frames), the overall accuracy of the model was 85.1%. For predicting the presence of TCFA, sensitivity was 81.1%, specificity was 98.8%, positive predictive value was 63.9%, and negative predictive value was 99.5%. Regarding the presence of fibroatheroma, sensitivity was 90.3%, specificity was 89.4%, positive predictive value was 93.1%, and negative predictive value was 85.4%. Conclusions The deep learning algorithm demonstrated high accuracy in detecting TCFA and fibroatheroma with excellent reproducibility. This automated process enables operators to promptly identify high-risk lesions during PCI, potentially leading to improved clinical outcomes.