Accurate classification of plaque composition is essential for treatment planning. Deep learning (DL) methods have been introduced for this purpose, to analyze intravascular images and characterize in a fast and subjective manner plaque types. In this study, we compared the efficacy of two DL methods, designed to process data acquired by two intravascular–an optical coherence tomography (OCT) and a near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS)–catheters to assess plaque types using histology as the reference standard. We matched histology, OCT, and NIRS-IVUS images, compared their estimations, and found that the DL method developed for NIRS-IVUS analysis had a better correlation with histology for calcific and lipidic tissue as compared with the OCT-DL method while both methods had a moderate correlation with the estimations of histology for fibrotic tissue. These findings could be attributed to the fact that OCT due to its poor penetration especially in lesions with large plaque burden fails to identify the deep-seated plaque and also to the fact that the NIRS-IVUS-DL method was developed with the use of histology instead of experts’ analysis.
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