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Computers, Volume 13, Issue 12

December 2024 - 43 articles

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Cover Story: Catheter ablation therapy for atrial fibrillation (AF) has a higher recurrence rate as the duration of AF increases. In this study, we used contrast-enhanced computed tomography (CT) to classify AF into paroxysmal AF (PAF) and long-term persistent AF (LSAF), which have different recurrence rates after catheter ablation. CT images of 30 PAF and 30 LSAF patients were input into six pretrained convolutional neural networks (CNNs) for binary classification. The classification was visualized using saliency maps based on score class activation mapping (CAM). The proposed method achieved an 81.7% classification accuracy. The results suggest that this method can classify AF more accurately than physicians, focusing on the left atrium shape, similar to physician judgment criteria. View this paper

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