Routine Echocardiography and Artificial Intelligence Solutions

Abstract

Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow.Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation.Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.

Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging

“…Essential issues are automatic screening of unread studies for potential emergency findings requiring urgent reporting and emergency treatment. Meanwhile, medical devices that provide fully automated quantitative assessments of images are commercially available for different modalities as echocardiography (22,23), cardiac magnetic resonance imaging (CMR), computed tomography (CCT) (24,25), nuclear cardiac imaging (25,26) and multimodality imaging (17,25).…”

“…The quality of explanations must be developed as a critical safety measure in HATs comprising MLMD (47). 22 Safe and effective implementation of ethical considerations into devices and their use may be achieved by embedding ethical values in the total product life-cycle. Targets in development are intended use, requirements, design, and architecture (48,49).…”

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

“…1) that is widely used for imaging applications. Trained CNNs have the ability to detect and classify distinctive features (e.g., edges of anatomical structures) on images, for example, to classify views of echocardiograms [3,11].…”

“…Artificial intelligence (AI) has an emerging role in healthcare in general, and the same holds for cardiology specifically, with numerous solutions in cardiac imaging modalities on image acquisition and reconstruction, diagnosis, and prognosis [1]. For example, AI applications are now being utilized to accelerate acquisition and reduce reconstruction time of cardiac MRI, to automate disease classification in echocardiography, and to improve conventional risk prediction models based on coronary CT angiography features [2][3][4][5]. Despite growing applications in general cardiology, the role of AI in automated analysis of invasive coronary angiography (ICA) is less clear.…”

How often are imaging studies for thoracic aortic aneurysm necessary?

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In recent years imaging studies have increased in number and complexity [1,2]. Consequently, imaging laboratories face the ongoing, growing challenge of processing all of the clips and images they obtain daily.

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