Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart conditions that can progress to heart failure if untreated. They exhibit similar echocardiographic characteristics, often leading to diagnostic challenges. This paper introduces a novel multi-view deep learning approach that utilizes 2D echocardiography for differentiating between HCM and CA. The method begins by classifying 2D echocardiography data into five distinct echocardiographic views: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at levels of the mitral valve, papillary muscle, and apex. It then extracts features of each view separately and combines five features for disease classification. A total of 212 patients diagnosed with HCM, and 30 patients diagnosed with CA, along with 200 individuals with normal cardiac function(Normal), were enrolled in this study from 2018 to 2022. This approach achieved a precision, recall of 0.905, and micro-F1 score of 0.904, demonstrating its effectiveness in accurately identifying HCM and CA using a multi-view analysis.

通过多视角分析，利用 2D 超声心动图来区分扩张型心肌病和心脏淀粉样变。该方法通过将 2D 超声心动图数据分类为五个不同的超声心动图视图，并分别提取每个视图的特征，在疾病分类中结合五个特征。研究结果表明该方法在准确识别扩张型心肌病和心脏淀粉样变方面具有很好的效果。

使用2D多视角心脏超声显像区分肥厚型心肌病和心脏淀粉样变性的深度学习驱动流程