![]() ![]() This method has a higher efficiency, which may be proven to be a promising diagnostic tool in the detection of coronary plaques.Ĭardiovascular disease remains to be the leading cause of morbidity and mortality globally. CNN with cascaded structure can greatly improve the performance of characterization compared to the conventional CNN methods and machine learning methods. The TwopathCNN architecture and cascaded structure show significant improvement in performance ( p < 0.05). On average, the method achieves 0.86 in F1-score and 0.88 in accuracy. According to the evaluation, this proposed method is effective and robust in the characterization of coronary plaque composition from in vivo OCT imaging. The method is based on a novel CNN architecture called TwopathCNN, which is utilized in a cascaded structure. This study aims to develop a convolutional neural network (CNN) method to automatically extract tissue features from OCT images to characterize the main components of a coronary atherosclerotic plaque (fibrous, lipid, and calcification). However, plaque characterization relies on the interpretation of large datasets by well-trained observers. 3School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australiaīackground: The morphological structure and tissue composition of a coronary atherosclerotic plaque determine its stability, which can be assessed by intravascular optical coherence tomography (OCT) imaging.2Department of Cardiology, Nanjing Drum Tower Hospital, Nanjing, China.1School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.Yifan Yin 1 Chunliu He 1 Biao Xu 2 Zhiyong Li 1,3 *
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