Abstract
Ultrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiography poses a challenge for sonographers to diagnose accurately. However, artificial intelligence for detection, functional evaluation, and disease diagnosis has gradually become an alternative for accurate diagnosis and treatment using echocardiography. This work discusses the current application of artificial intelligence in echocardiography technology, its limitations, and future development directions.
Highlights
Standard section recognition with assistance of artificial intelligence (AI) technology The anatomical structure of the heart is complex, and sonogram genres are disparate
AI technology has been progressively applied for processing multiple modal images, such as auxiliary electrocardiograph diagnosis [2], cardiac computerized tomography (CT) detection [3], and radionuclide myocardial perfusion imaging
Before deep learning was proposed in 2006, plenty of machine learning algorithms had been applied to echocardiographic evaluation of cardiac function, image optimization, and structural observation in the form of software or cutting edge technology, such as semi-automatic speckle tracking technology and the Simpson method
Summary
The application of artificial intelligence (AI) technology in cardiovascular imaging has become a research hotspot in recent years, as it may reduce treatment cost and help avoid unnecessary testing [1]. Earlier cases of integrated application of echocardiography and machine learning can be traced back to 1978 when Fourier analysis was used to evaluate the waveform of anterior mitral leaflets via M-mode ultrasound. Before deep learning was proposed in 2006, plenty of machine learning algorithms had been applied to echocardiographic evaluation of cardiac function, image optimization, and structural observation in the form of software or cutting edge technology, such as semi-automatic speckle tracking technology and the Simpson method. The development of novel technologies, such as deep learning and neural networks, has effectively improved the efficacy of echocardiography [9], making standard section identification of cardiac anatomical structures, automatic recognition and segmentation of cardiac structures, cardiac functional evaluation, and auxiliary disease diagnosis faster and more accurate [10, 11] (Fig. 1). This review summarizes the application (Table 1) and advantages of echocardiography integrated with AI, analyzes the associated limitations, and systematically investigates the future trends of AI technology in echocardiography from the perspective of practical applications
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