Literature DB >> 31171260

Automated Quantification in Echocardiography.

Mark T Nolan1, Paaladinesh Thavendiranathan2.   

Abstract

Echocardiography remains the predominant modality for cardiac imaging. Recent technological advances have led to the availability of new echocardiographic techniques for more accurate quantification of volumes, function, myocardial mechanics, and valvular heart disease. However, in our opinion, the real-world clinical uptake of these techniques has been poor due to limited awareness and familiarity, associated time burden, and issues of variability. Automation represents a potential solution to these issues and has already made routine myocardial strain measurements and 2- and 3-dimensional left ventricular ejection fraction measurements a clinical reality. Further enhancements in automation and data in understudied populations are likely to assist in the uptake of these new quantitative echocardiographic techniques in routine clinical practice. This review discusses current automated quantification techniques in echocardiography and their limitations and describes how these techniques can be incorporated into echocardiography laboratories.
Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  automation; echocardiography

Mesh:

Year:  2019        PMID: 31171260     DOI: 10.1016/j.jcmg.2018.11.038

Source DB:  PubMed          Journal:  JACC Cardiovasc Imaging        ISSN: 1876-7591


  8 in total

Review 1.  Artificial Intelligence and Machine Learning in Cardiovascular Imaging.

Authors:  Karthik Seetharam; James K Min
Journal:  Methodist Debakey Cardiovasc J       Date:  2020 Oct-Dec

2.  Reliability and feasibility of automated function imaging for quantification in patients with left ventricular dilation: comparison with cardiac magnetic resonance.

Authors:  Yefen Chen; Wei Hua; Wenbo Yang; Zhongwei Shi; Yuehua Fang
Journal:  Int J Cardiovasc Imaging       Date:  2022-01-03       Impact factor: 2.357

Review 3.  Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment.

Authors:  Zisang Zhang; Ye Zhu; Manwei Liu; Ziming Zhang; Yang Zhao; Xin Yang; Mingxing Xie; Li Zhang
Journal:  J Clin Med       Date:  2022-05-20       Impact factor: 4.964

Review 4.  Aortic Annular Sizing Using Novel Software in Three-Dimensional Transesophageal Echocardiography for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.

Authors:  Chanrith Mork; Minjie Wei; Weixi Jiang; Jianli Ren; Haitao Ran
Journal:  Diagnostics (Basel)       Date:  2021-04-22

5.  Elucidating tricuspid Doppler signal interpolation and its implication for assessing pulmonary hypertension.

Authors:  Seraina A Dual; Constance Verdonk; Myriam Amsallem; Jonathan Pham; Courtney Obasohan; Patrick Nataf; Doff B McElhinney; Alisa Arunamata; Tatiana Kuznetsova; Roham Zamanian; Jeffrey A Feinstein; Alison Marsden; François Haddad
Journal:  Pulm Circ       Date:  2022-07-01       Impact factor: 2.886

6.  Three-Dimensional Echocardiography Based on Automation and Machine Learning Principles and the Renaissance of Cardiac Morphometry.

Authors:  Andrea Barbieri; Mauro Pepi
Journal:  J Clin Med       Date:  2022-07-27       Impact factor: 4.964

7.  Real-time echocardiography image analysis and quantification of cardiac indices.

Authors:  Ghada Zamzmi; Sivaramakrishnan Rajaraman; Li-Yueh Hsu; Vandana Sachdev; Sameer Antani
Journal:  Med Image Anal       Date:  2022-06-09       Impact factor: 13.828

Review 8.  Artificial intelligence for the echocardiographic assessment of valvular heart disease.

Authors:  Rashmi Nedadur; Bo Wang; Wendy Tsang
Journal:  Heart       Date:  2022-09-26       Impact factor: 7.365

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.