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Coronary Artery Identification on Echocardiograms for Kawasaki Disease Diagnosis

机译:川崎病诊断超声心动造影冠状动脉鉴定

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Kawasaki disease’s consequences may result in vasculitis, myocarditis and coronary dilatation causing long term heart complications by damaging blood vessels all over the body. It is the most common acquired heart condition affecting young children in developed countries. Follow up of Kawasaki disease patients is done by 2D echocardiograms in order to detect coronary artery abnormalities. Such inspection is very difficult to automatize and has to be done manually since the size and shape of the heart in young children varies significantly. We present a solution to ease and speed-up the diagnosis of Kawasaki disease based on Convolutional Neural Networks. More specifically, our work can automatically detect which frames of a 2D echocardiogram contain coronary arteries. A Convolutional Neural Network has been designed with this purpose, and its performance has been compared to those of VGG16 and Resnet50 networks. To evaluate our approach a specific echocardiogram dataset for Kawasaki disease has been created in collaboration with 12 de Octubre Hospital in Madrid. This solution can be considered as a first step in the development of a fully automated solution for its diagnosis.
机译:川崎病的后果可能导致血管炎,心肌炎和冠状动脉扩张导致长期心脏并发症,通过损坏身体损坏血管。它是影响发达国家幼儿的最常见的心脏病。川崎病患者的跟随由2D超声心动图进行,以检测冠状动脉异常。这种检查非常难以自动化,并且必须手动完成,因为幼儿心脏的大小和形状显着变化。我们提出了一种解决方案,可以缓解和加速基于卷积神经网络的川崎病的诊断。更具体地,我们的工作可以自动检测2D超声心动图的帧含有冠状动脉。卷积神经网络已经设计为本用途,它的性能与VGG16和Reset50网络的性能进行了比较。为了评估我们的方法,川崎病的特定超声心动图数据集是在马德里的12 de Octubre医院合作中创建的。该解决方案可以被认为是开发完全自动化解决方案的诊断的第一步。

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