Automated comprehensive evaluation of coronary artery plaque in IVOCT using deep learning.

The process of manually characterizing and quantifying coronary artery plaque tissue in intravascular optical coherence tomography (IVOCT) images is both time-consuming and subjective. We have developed a deep learning-based semantic segmentation model (EDA-UNet) designed specifically for characterizing and quantifying coronary artery plaque tissue in IVOCT images. IVOCT images from two centers were utilized as the internal dataset for model training and internal testing. Images from another independent center employing IVOCT were used for external testing. The Dice coefficients for fibrous plaque, calcified plaque, and lipid plaque in external tests were 0.8282, 0.7408, and 0.7052 respectively. The model demonstrated strong correlation and consistency with the ground truth in the quantitative analysis of calcification scores and the identification of thin-cap fibroatheroma (TCFA). The median duration for each callback analysis was 18 s. EDA-UNet model serves as an efficient and accurate technological tool for plaque characterization and quantification.