Literature DB >> 29974498

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

Sumeet Gandhi1,2, Wassim Mosleh3, Joshua Shen2, Chi-Ming Chow2.   

Abstract

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.
© 2018 Wiley Periodicals, Inc.

Entities:  

Keywords:  algorithm; artificial intelligence; automation; deep learning; echocardiography; machine learning

Mesh:

Year:  2018        PMID: 29974498     DOI: 10.1111/echo.14086

Source DB:  PubMed          Journal:  Echocardiography        ISSN: 0742-2822            Impact factor:   1.724


  17 in total

Review 1.  Automation in ART: Paving the Way for the Future of Infertility Treatment.

Authors:  Kadrina Abdul Latif Abdullah; Tomiris Atazhanova; Alejandro Chavez-Badiola; Sourima Biswas Shivhare
Journal:  Reprod Sci       Date:  2022-08-03       Impact factor: 2.924

2.  Challenges associated with retrospective analysis of left ventricular function using clinical echocardiograms from a multicenter research study.

Authors:  Ritu Sachdeva; Kayla L Stratton; David E Cox; Saro H Armenian; Aarti Bhat; William L Border; Kasey J Leger; Wendy M Leisenring; Lillian R Meacham; Karim T Sadak; Shanti Narasimhan; Eric J Chow; Paul C Nathan
Journal:  Echocardiography       Date:  2021-01-24       Impact factor: 1.724

Review 3.  Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease.

Authors:  Nobuyuki Kagiyama; Sirish Shrestha; Peter D Farjo; Partho P Sengupta
Journal:  J Am Heart Assoc       Date:  2019-08-27       Impact factor: 5.501

4.  An optimisation-based iterative approach for speckle tracking echocardiography.

Authors:  Neda Azarmehr; Xujiong Ye; Joseph D Howes; Benjamin Docking; James P Howard; Darrel P Francis; Massoud Zolgharni
Journal:  Med Biol Eng Comput       Date:  2020-04-07       Impact factor: 2.602

5.  Left ventricular strain values using 3D speckle-tracking echocardiography in healthy adults aged 20 to 72 years.

Authors:  Ferit Onur Mutluer; Daniel J Bowen; Roderick W J van Grootel; Jolien W Roos-Hesselink; Annemien E Van den Bosch
Journal:  Int J Cardiovasc Imaging       Date:  2020-11-23       Impact factor: 2.357

Review 6.  Deep Learning for Cardiac Image Segmentation: A Review.

Authors:  Chen Chen; Chen Qin; Huaqi Qiu; Giacomo Tarroni; Jinming Duan; Wenjia Bai; Daniel Rueckert
Journal:  Front Cardiovasc Med       Date:  2020-03-05

7.  A machine learning approach for the prediction of pulmonary hypertension.

Authors:  Andreas Leha; Kristian Hellenkamp; Bernhard Unsöld; Sitali Mushemi-Blake; Ajay M Shah; Gerd Hasenfuß; Tim Seidler
Journal:  PLoS One       Date:  2019-10-25       Impact factor: 3.240

Review 8.  Artificial intelligence and cardiovascular imaging: A win-win combination.

Authors:  Luigi P Badano; Daria M Keller; Denisa Muraru; Camilla Torlasco; Gianfranco Parati
Journal:  Anatol J Cardiol       Date:  2020-10       Impact factor: 1.596

9.  Adversarial attack on deep learning-based dermatoscopic image recognition systems: Risk of misdiagnosis due to undetectable image perturbations.

Authors:  Jérôme Allyn; Nicolas Allou; Charles Vidal; Amélie Renou; Cyril Ferdynus
Journal:  Medicine (Baltimore)       Date:  2020-12-11       Impact factor: 1.817

Review 10.  Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications.

Authors:  Chris Boyd; Greg Brown; Timothy Kleinig; Joseph Dawson; Mark D McDonnell; Mark Jenkinson; Eva Bezak
Journal:  Diagnostics (Basel)       Date:  2021-03-19
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