Literature DB >> 33500754

Artificial Intelligence and Machine Learning in Cardiovascular Imaging.

Karthik Seetharam1, James K Min1.   

Abstract

Cardiovascular disease is the leading cause of mortality in Western countries and leads to a spectrum of complications that can complicate patient management. The emergence of artificial intelligence (AI) has garnered significant interest in many industries, and the field of cardiovascular imaging is no exception. Machine learning (ML) especially is showing significant promise in various diagnostic imaging modalities. As conventional statistics are reaching their apex in computational capabilities, ML can explore new possibilities and unravel hidden relationships. This will have a positive impact on diagnosis and prognosis for cardiovascular imaging. In this in-depth review, we highlight the role of AI and ML for various cardiovascular imaging modalities.
© 2020 Houston Methodist Hospital Houston, Texas.

Entities:  

Keywords:  artificial intelligence; deep learning algorithms; machine learning

Year:  2020        PMID: 33500754      PMCID: PMC7812848          DOI: 10.14797/mdcj-16-4-263

Source DB:  PubMed          Journal:  Methodist Debakey Cardiovasc J        ISSN: 1947-6108


  47 in total

Review 1.  Deep Learning in Cardiology.

Authors:  Paschalis Bizopoulos; Dimitrios Koutsouris
Journal:  IEEE Rev Biomed Eng       Date:  2018-12-10

Review 2.  The Role of Artificial Intelligence in Echocardiography.

Authors:  Karthik Seetharam; Sameer Raina; Partho P Sengupta
Journal:  Curr Cardiol Rep       Date:  2020-07-30       Impact factor: 2.931

3.  Chess and Coronary Artery Ischemia: Clinical Implications of Machine-Learning Applications.

Authors:  James K Min
Journal:  Circ Cardiovasc Imaging       Date:  2018-06       Impact factor: 7.792

4.  ν-net: Deep Learning for Generalized Biventricular Mass and Function Parameters Using Multicenter Cardiac MRI Data.

Authors:  Hinrich B Winther; Christian Hundt; Bertil Schmidt; Christoph Czerner; Johann Bauersachs; Frank Wacker; Jens Vogel-Claussen
Journal:  JACC Cardiovasc Imaging       Date:  2018-01-17

5.  Computational Platform Based on Deep Learning for Segmenting Ventricular Endocardium in Long-axis Cardiac MR Imaging.

Authors:  Shuang Leng; Xulei Yang; Xiaodan Zhao; Zeng Zeng; Yi Su; Angela S Koh; David Sim; Ju Le Tan; Ru San Tan; Liang Zhong
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2018-07

6.  Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.

Authors:  Julian Betancur; Frederic Commandeur; Mahsaw Motlagh; Tali Sharir; Andrew J Einstein; Sabahat Bokhari; Mathews B Fish; Terrence D Ruddy; Philipp Kaufmann; Albert J Sinusas; Edward J Miller; Timothy M Bateman; Sharmila Dorbala; Marcelo Di Carli; Guido Germano; Yuka Otaki; Balaji K Tamarappoo; Damini Dey; Daniel S Berman; Piotr J Slomka
Journal:  JACC Cardiovasc Imaging       Date:  2018-03-14

7.  Stress myocardial perfusion single-photon emission computed tomography is clinically effective and cost effective in risk stratification of patients with a high likelihood of coronary artery disease (CAD) but no known CAD.

Authors:  Rory Hachamovitch; Sean W Hayes; John D Friedman; Ishac Cohen; Daniel S Berman
Journal:  J Am Coll Cardiol       Date:  2004-01-21       Impact factor: 24.094

8.  Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.

Authors:  Manish Motwani; Damini Dey; Daniel S Berman; Guido Germano; Stephan Achenbach; Mouaz H Al-Mallah; Daniele Andreini; Matthew J Budoff; Filippo Cademartiri; Tracy Q Callister; Hyuk-Jae Chang; Kavitha Chinnaiyan; Benjamin J W Chow; Ricardo C Cury; Augustin Delago; Millie Gomez; Heidi Gransar; Martin Hadamitzky; Joerg Hausleiter; Niree Hindoyan; Gudrun Feuchtner; Philipp A Kaufmann; Yong-Jin Kim; Jonathon Leipsic; Fay Y Lin; Erica Maffei; Hugo Marques; Gianluca Pontone; Gilbert Raff; Ronen Rubinshtein; Leslee J Shaw; Julia Stehli; Todd C Villines; Allison Dunning; James K Min; Piotr J Slomka
Journal:  Eur Heart J       Date:  2017-02-14       Impact factor: 29.983

9.  Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Authors:  Donghee Han; Kranthi K Kolli; Subhi J Al'Aref; Lohendran Baskaran; Alexander R van Rosendael; Heidi Gransar; Daniele Andreini; Matthew J Budoff; Filippo Cademartiri; Kavitha Chinnaiyan; Jung Hyun Choi; Edoardo Conte; Hugo Marques; Pedro de Araújo Gonçalves; Ilan Gottlieb; Martin Hadamitzky; Jonathon A Leipsic; Erica Maffei; Gianluca Pontone; Gilbert L Raff; Sangshoon Shin; Yong-Jin Kim; Byoung Kwon Lee; Eun Ju Chun; Ji Min Sung; Sang-Eun Lee; Renu Virmani; Habib Samady; Peter Stone; Jagat Narula; Daniel S Berman; Jeroen J Bax; Leslee J Shaw; Fay Y Lin; James K Min; Hyuk-Jae Chang
Journal:  J Am Heart Assoc       Date:  2020-02-22       Impact factor: 5.501

10.  Fully Automated Echocardiogram Interpretation in Clinical Practice.

Authors:  Jeffrey Zhang; Sravani Gajjala; Pulkit Agrawal; Geoffrey H Tison; Laura A Hallock; Lauren Beussink-Nelson; Mats H Lassen; Eugene Fan; Mandar A Aras; ChaRandle Jordan; Kirsten E Fleischmann; Michelle Melisko; Atif Qasim; Sanjiv J Shah; Ruzena Bajcsy; Rahul C Deo
Journal:  Circulation       Date:  2018-10-16       Impact factor: 29.690

View more
  1 in total

Review 1.  Applications of Machine Learning in Cardiology.

Authors:  Karthik Seetharam; Sudarshan Balla; Christopher Bianco; Jim Cheung; Roman Pachulski; Deepak Asti; Nikil Nalluri; Astha Tejpal; Parvez Mir; Jilan Shah; Premila Bhat; Tanveer Mir; Yasmin Hamirani
Journal:  Cardiol Ther       Date:  2022-07-12
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.