For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
0
views
14
references
 Top references
cited by
18
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      151
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: not found

      Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Artificial intelligence (AI) is emerging as a key component in diagnostic medical imaging, including echocardiography. AI with deep learning has already been used with automated view labeling, measurements, and interpretation. As the development and use of AI in echocardiography increase, potential concerns may be raised by cardiac sonographers and the profession. This report, from a sonographer's perspective, focuses on defining AI, the basics of the technology, identifying some current applications of AI, and how the use of AI may improve patient care in the future.

          Highlights

          • •

            AI will have a strong role in echocardiography.

          • •

            AI will guide image acquisition and optimization.

          • •

            AI for image analysis may aid in interpretation.

          • •

            AI is a tool that will not replace sonographers but will help them be more efficient.

          Related collections

          Most cited references14

          • Record: found
          • Abstract: not found
          • Article: not found

          Guidelines for Performing a Comprehensive Transthoracic Echocardiographic Examination in Adults: Recommendations from the American Society of Echocardiography

          Carol Mitchell, Peter Rahko, Lori Blauwet … (2018)
          Article 0 comments Cited 546 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Some Studies in Machine Learning Using the Game of Checkers

            A. L. Samuel (1959)
            Article 0 comments Cited 521 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Artificial intelligence and echocardiography

              M Alsharqi, W Woodward, J Mumith … (2018)
              Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.
              Article 0 comments Cited 49 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Ashlee Davis
                Journal
                Journal ID (nlm-ta): J Am Soc Echocardiogr
                Journal ID (iso-abbrev): J Am Soc Echocardiogr
                Title: Journal of the American Society of Echocardiography
                Publisher: Mosby-Year Book
                ISSN (Print): 0894-7317
                ISSN (Electronic): 1097-6795
                Publication date PMC-release: 11 June 2020
                Publication date (Electronic): 11 June 2020
                Affiliations
                [a ]Duke Health System, Durham, North Carolina
                [b ]Scripps Clinic, La Jolla, California
                [c ]Intermountain Healthcare, Salt Lake City, Utah
                [d ]Northwestern Medicine, Chicago, Illinois
                [e ]University of California, Irvine, Irvine, California
                [f ]Massachusetts General Hospital, Boston, Massachusetts
                [g ]Stanford Healthcare, Stanford, California
                Author notes
                [∗ ]Reprint requests: Ashlee Davis, BS, ACS, RDCS, FASE, Duke Health System, Duke University Hospital, Cardiac Diagnostic Unit, 2301 Erwin Road, Durham, NC 27710. ashlee.phillips@ 123456duke.edu
                Article
                Publisher ID: S0894-7317(20)30256-X
                DOI: 10.1016/j.echo.2020.04.025
                PMC ID: 7289098
                PubMed ID: 32536431
                SO-VID: 419643ab-d33a-44bd-a624-5d96a40e3b23
                Copyright © 2020 by the American Society of Echocardiography.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Categories
                Subject: Article

                ScienceOpen disciplines: Cardiovascular Medicine
                Keywords: artificial intelligence,echocardiography,neural networks,deep learning,machine learning,ai, artificial intelligence,cnn, convolutional neural network,ef, ejection fraction
                Data availability:
                ScienceOpen disciplines: Cardiovascular Medicine
                Keywords: artificial intelligence, echocardiography, neural networks, deep learning, machine learning, ai, artificial intelligence, cnn, convolutional neural network, ef, ejection fraction

                Comments

                Comment on this article

                scite_

                Similar content151

                See all similar

                Cited by18

                See all cited by

                Most referenced authors224

                See all reference authors