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Current Challenges and Recent Updates in Artificial Intelligence and Echocardiography

  • Echocardiography (G Dwivedi, Section Editor)
  • Published:
Current Cardiovascular Imaging Reports Aims and scope Submit manuscript

Abstract

Purpose of the Review

This review discusses the recent advances in automated echocardiography using artificial intelligence and machine learning (ML) techniques. Specific emphasis is placed on the potential for machine learning-based methods to improve accuracy and reproducibility of echocardiographic assessment as well as early cardiovascular disease detection and personalized risk assessment.

Recent Findings

Echocardiography remains the first line imaging modality for evaluation of many cardiovascular diseases. The last few years have witnessed a rapid expansion and growth of ML-based automated analysis and interpretation of echocardiography. These ML algorithms have shown great promise for improving data reliability, accuracy, and reproducibility of echocardiographic results. We anticipate that the application of ML algorithms will further expand the indications of echocardiography to include diseases that are traditionally only diagnosed with the more advanced imaging modalities such as cardiac magnetic resonance imaging. The ability to leverage ML’s robust capability for processing large and complex datasets will result in improved diagnosis of cardiovascular disease at subclinical stages, enable prediction of disease progression and prognosis, and facilitate the characterization of disease phenotypes to allow more targeted therapies.

Summary

The paradigm is rapidly shifting in the field of echocardiography with the emergence of ML algorithms that are promising to improve data reliability, accuracy, reproducibility, and workflow. Current and emerging evidence suggests that these systems will undoubtedly revolutionize the diagnostic utility of echocardiography both at subclinical and clinical stages and are expected to improve personalized cardiovascular risk assessment. However, widespread implementation of this novel technology will need to overcome challenging regulatory body approval processes. At present, the technology shows promise in improving diagnostic pathways, but evidence of clinical utility is lacking. Large trials will be required to provide robust evidence of ML’s prognostic value in echocardiographic assessment before its implementation in routine clinical practice.

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Author notes

  1. Nestor Gahungu and Robert Trueick contributed equally to this work.

Authors and Affiliations

  1. Royal Perth Hospital, Victoria Square, Perth, WA, 6000, Australia

    Nestor Gahungu & Robert Trueick

  2. School of Medicine, The University of Western Australia, 35 Stirling Hwy, Crawley, WA, 6009, Australia

    Saiuj Bhat & Girish Dwivedi

  3. Division of Cardiology, West Virginia Heart and Vascular Institute, West Virginia University School of Medicine, Morgantown, WV, USA

    Partho P. Sengupta

  4. Department of Cardiology, Fiona Stanley Hospital, Murdoch Drive, Murdoch, WA, 6150, Australia

    Girish Dwivedi

  5. Department of Advanced Clinical and Translational Cardiovascular Imaging, Harry Perkins Institute of Medical Research, Murdoch Drive, Murdoch, WA, 6150, Australia

    Girish Dwivedi

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  1. Nestor Gahungu
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Gahungu, N., Trueick, R., Bhat, S. et al. Current Challenges and Recent Updates in Artificial Intelligence and Echocardiography. Curr Cardiovasc Imaging Rep 13, 5 (2020). https://doi.org/10.1007/s12410-020-9529-x

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