Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning.

Lingxia Zhu,Ting Fang,Zhiping Xu

doi:10.1155/2021/6050433

Abstract

Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasound images of critically ill cardiac patients. A convolution neural network (CNN) is used to classify the standard ultrasound video data. The ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. The correlation between the ultrasound video data frames is used to construct the ResNet50 + LSTM model. Next, the time-series features of the two-dimensional image sequence are extracted and the classification of the ultrasound section video data is realized. Experimental results show that the proposed cardiac ultrasound image recognition model has good performance and can meet the requirements of clinical section classification accuracy.

Highlights

Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too
A convolution neural network (CNN) is used to classify the standard ultrasound video data. e ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. e correlation between the ultrasound video data frames is used to construct the ResNet50 + long- and short-term memory (LSTM) model
Datasets. e ultrasound video data used in this article is obtained from the Department of Cardiovascular Medicine of a tertiary hospital, including eight standard views: apical two-chamber view (A2C), apical three-chamber view (A3C), apical four-chamber view (A4C), parasternal apical level left ventricle short-axis view (ASA), parasternal aorta short-axis view (BSA), parasternal mitral valve level left ventricular short-axis view (MSA), parasternal papillary muscle level left ventricle short-axis view (PSA), and parasternal left long-axis view of the ventricle (PLA). ese views are labeled as A2C-0, A3C-1, A4C-2, ASA-3, BSA-4, MSA-5, PLA-6, and PSA-7, respectively. is dataset is used to train, verify, and test the model. e dataset is comprised of 3378 ultrasound video data points of 1,413 patients, which are all in the medically standard DICOM format

Summary

Introduction

Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. Ultrasound imaging diagnosis has the characteristics of noninvasive, nonradiation, high time resolution, and measurable blood flow It is the most commonly used and most important mode in current cardiac examinations. E structural information inside the heart, such as the heart chambers, ventricular walls, and large blood vessels, expressed by these cross-sectional images forms the basis of cardiac ultrasound diagnosis. E long-axis plane is the ultrasound inspection plane obtained by connecting the right sternoclavicular joint and the left nipple, the short-axis plane is the inspection plane with an angle of 90° to the long-axis plane of the heart, and the four-chamber plane is simultaneous with the longaxis plane and the short-axis plane Based on these three basic imaging planes, a variety of echocardiographic slices are derived according to different probe angles. Based on these three basic imaging planes, a variety of echocardiographic slices are derived according to different probe angles. ese slices include the fundus short-axis slice, the apical short-axis slice, the mitral valve short-axis slice, the papillary muscle shortaxis slice, apical two-chamber view, apical three-chamber view, and apical four-chamber view, which are the seven main sections [6,7,8,9,10]

Methods

Results

Conclusion