使用2D多视图超声心动图区分肥厚型心肌病与心脏淀粉样变性的深度学习驱动流程
A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography
AI总结：
提出一种基于2D多视图超声心动图的深度学习方法，通过分类五种切面并融合特征，有效区分肥厚型心肌病与心脏淀粉样变性，精确率达0.905。
摘要：
肥厚型心肌病（HCM）和心脏淀粉样变性（CA）都是心脏疾病，如果不治疗可能发展为心力衰竭。它们表现出相似的超声心动图特征，常常导致诊断困难。本文提出了一种新颖的多视图深度学习方法，利用2D超声心动图来区分HCM和CA。该方法首先将2D超声心动图数据分类为五种不同的超声心动图视图：心尖四腔心切面、左心室长轴切面、二尖瓣水平、乳头肌水平和心尖水平的胸骨旁短轴切面。然后分别提取每个视图的特征，并将五个特征结合起来进行疾病分类。本研究从2018年至2022年共纳入了212名诊断为HCM的患者、30名诊断为CA的患者，以及200名心功能正常的个体（Normal）。该方法实现了0.905的精确率、召回率和0.904的micro-F1分数，证明了其在使用多视图分析准确识别HCM和CA方面的有效性。
英文摘要：
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.
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