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.

翻译：肥厚型心肌病（HCM）与心脏淀粉样变性（CA）若未及时治疗，均可进展为心力衰竭。两者具有相似的超声心动图特征，常导致诊断困难。本文提出了一种基于多视角深度学习的创新方法，利用二维超声心动图鉴别HCM与CA。该方法首先将二维超声心动图数据分类为五种标准切面：心尖四腔心、胸骨旁左心室长轴、二尖瓣水平胸骨旁短轴、乳头肌水平胸骨旁短轴及心尖水平胸骨旁短轴；随后分别提取各切面特征，并融合五类特征进行疾病分类。本研究纳入2018至2022年间确诊的212例HCM患者、30例CA患者及200例心功能正常者，该方法实现了精确率0.905、召回率0.905及微平均F1分数0.904，验证了多视角分析在准确识别HCM与CA中的有效性。