Combined intravascular ultrasound-optical coherence tomography (IVUS-OCT) enables more accurate coronary plaque tissue classification compared to single modality systems. Automated solutions are needed to that take advantage of information from both modalities to speed such analysis. This study aimed to train and validate a deep learning (DL) model for tissue classification in combined IVUS-OCT images. Coronary segments from 8 arteries from cadaveric human hearts were studied with the Novasight Hybrid imaging catheter. IVUS-OCT images were matched with histological sections and tissue types annotated. These regions of interest were used train and test a DL-classifier for plaque composition (949 matched histological and IVUS-OCT frames from 8 patients for training, 306 frames from 2 patients for testing). The accuracy of the classifier for regional classification was 78.8% suggesting that the trained DL-model is capable of accurate tissue type classification in combined IVUS-OCT images.
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