Automation, machine learning, and artificial intelligence in echocardiography: A brave new world

Automation, machine learning, and artificial intelligence (AI) are changing the landscape of echocardiography providing complimentary tools to physicians to enhance patient care. Multiple vendor software programs have incorporated automation to improve accuracy and efficiency of manual tracings. Aut...

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Bibliographic Details
Published in:Echocardiography (Mount Kisco, N.Y.) N.Y.), 2018-09, Vol.35 (9), p.1402-1418
Main Authors: Gandhi, Sumeet, Mosleh, Wassim, Shen, Joshua, Chow, Chi‐Ming
Format: Article
Language:English
Subjects:
algorithm
Algorithms
Artificial Intelligence
automation
Automation - methods
deep learning
echocardiography
Echocardiography - methods
Heart Diseases - diagnostic imaging
Humans
Image Interpretation, Computer-Assisted - methods
Machine Learning
Reproducibility of Results
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Summary:Automation, machine learning, and artificial intelligence (AI) are changing the landscape of echocardiography providing complimentary tools to physicians to enhance patient care. Multiple vendor software programs have incorporated automation to improve accuracy and efficiency of manual tracings. Automation with longitudinal strain and 3D echocardiography has shown great accuracy and reproducibility allowing the incorporation of these techniques into daily workflow. This will give further experience to nonexpert readers and allow the integration of these essential tools into more echocardiography laboratories. The potential for machine learning in cardiovascular imaging is still being discovered as algorithms are being created, with training on large data sets beyond what traditional statistical reasoning can handle. Deep learning when applied to large image repositories will recognize complex relationships and patterns integrating all properties of the image, which will unlock further connections about the natural history and prognosis of cardiac disease states. The purpose of this review article was to describe the role and current use of automation, machine learning, and AI in echocardiography and discuss potential limitations and challenges of in the future.
ISSN:0742-2822
1540-8175
DOI:10.1111/echo.14086