首页|Intelligent diagnosis of atrial septal defect in children using echocardiography with deep learning

Intelligent diagnosis of atrial septal defect in children using echocardiography with deep learning

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Background Atrial septal defect(ASD)is one of the most common congenital heart diseases.The diagnosis of ASD via transthoracic echocardiography is subjective and time-consuming.Methods The objective of this study was to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic static images using end-to-end convolutional neural networks.The proposed depthwise separable convolution model identifies ASDs with static color Doppler images in a standard view.Among the standard views,we selected two echocardiographic views,i.e.,the subcostal sagittal view of the atrium septum and the low parasternal four-chamber view.The developed ASD detection system was validated using a training set consisting of 396 echocardiographic images corresponding to 198 cases.Additionally,an independent test dataset of 112 images corresponding to 56 cases was used,including 101 cases with ASDs and 153 cases with normal hearts.Results The average area under the receiver operating characteristic curve,recall,precision,specificity,F1-score,and accuracy of the proposed ASD detection model were 91.99,80.00,82.22,87.50,79.57,and 83.04,respectively.Conclusions The proposed model can accurately and automatically identify ASD,providing a strong foundation for the intelligent diagnosis of congenital heart diseases.

Deep learningAtrial septal defectEchocardiography

Yiman LIU、Size HOU、Xiaoxiang HAN、Tongtong LIANG、Menghan HU、Xin WANG、Wei GU、Yuqi ZHANG、Qingli LI、Jiangang CHEN

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Shanghai Key Laboratory of Multidimensional Information Processing,School of Communication&Electronic Engineering,East China Normal University,Shanghai,China

Department of Pediatric Cardiology,Shanghai Children's Medical Center,School of Medicine,Shanghai Jiao Tong University,Shanghai,China

Department of Applied Mathematics,Xi'an Jiaotong-Liverpool University,School of Science,Suzhou,China

School of Health Sciences and Engineering,University of Shanghai for Science and Technology,Shanghai,China

Minhang District Centers for Disease Control and Prevention,Shanghai,China

Faculty of Traditional Chinese Medicine,Naval Medical University,Shanghai,China

Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation,Ministry of Education,Shanghai 201203,China

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2024

10.1016/j.vrih.2023.05.002

虚拟现实与智能硬件(中英文)

虚拟现实与智能硬件(中英文)

ISSN：
年,卷(期)：2024.6(3)