Deep-learning based analysis of intracoronary optical coherence tomography images: detection and characterization of lipid plaques

Abstract

Abstract Background Intracoronary optical coherence tomography (OCT) enables in vivo detection and characterization of atherosclerotic disease. However, manual assessment of OCT images is time-consuming and subject to intra- and interobserver variability. Therefore, automated and trustworthy methods for plaque assessment are warranted. Purpose To develop and validate an artificial intelligence based algorithm for detection and characterization of lipid plaques on OCT images. Methods From the prospective, observational PECTUS-obs study, we studied a representative subset of OCT pullbacks performed in fractional flow reserve-negative non-culprit lesions in patients presenting with myocardial infarction. Manual segmentation of cross-sectional frames (e.g. lumen, tunica intima including the fibrous cap, tunica media and lipid pools) was performed by trained experts. Pullbacks were randomly divided into a training and test set. For automated segmentation and analysis, a two-dimensional no-new U-shaped neural network (nnUNet) was constructed with 5-fold cross validation. To test the diagnostic performance of the nnUnet for detection of lipid plaques, sensitivity, specificity and Cohen’s kappa were calculated. As for plaque characterization, the lipid arc and minimal fibrous cap thickness were measured manually in the test set on each frame containing a lipid plaque. Values of lipid arc and minimal fibrous cap thickness obtained following automated assessment were compared to manual analysis using the intraclass correlation coefficient (ICC) for absolute agreement. Results In total, 1215 frames were used for training and 162 frames for testing of the nnUNet. Lipid plaques were present in 77 frames (47.5%) in the test set. Substantial agreement (κ=0.79) for detection of a lipid plaque was achieved with a sensitivity and specificity of 96.1% and 83.5%, respectively. As for plaque characterization, we found good reliability for lipid arc assessment (ICC 0.88, mean difference 6±32º) and moderate reliability for assessment of the minimal fibrous cap thickness (ICC 0.54, mean difference 23±140µm). Conclusion We developed a deep learning algorithm for automated analysis of intracoronary OCT images, that was able to accurately detect lipid plaques. Moreover, the algorithm showed promising results in terms of plaque characterization. With further refinements, the developed algorithm has the potential to reduce the time required for OCT interpretation, ultimately enabling more efficient, real-time use of this imaging modality in clinical practice.