1. Detection of Optical Coherence Tomography–Defined Thin-Cap Fibroatheroma in the Coronary Artery Using Deep Learning

    Detection of Optical Coherence Tomography–Defined Thin-Cap Fibroatheroma in the Coronary Artery Using Deep Learning

    Aims: . To develop a deep learning model for classifying frames with vs. without optical coherence tomography (OCT)-derived thin-cap fibroatheroma (TCFA). Methods and results: . Total 602 coronary lesions from 602 angina patients were randomized into training and test sets at a 4:1 ratio. A DenseNet model was developed to classify OCT frames with or without OCT-derived TCFA. Gradient-weighted class activation mapping was used to visualize the area of attention. In the training sample (35,678 frames of 480 lesions), the model with 5-fold cross-validation had an overall accuracy of 91.6±1.7%, sensitivity of 88.7±3.4 ...

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