Literature DB >> 32510252

Artificial intelligence: improving the efficiency of cardiovascular imaging.

Andrew Lin1, Márton Kolossváry2, Ivana Išgum3,4,5, Pál Maurovich-Horvat2,6, Piotr J Slomka7, Damini Dey1.   

Abstract

INTRODUCTION: Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or phenotypes, or guide treatment strategies. Noninvasive imaging remains a cornerstone for the diagnosis, risk stratification, and management of patients with cardiovascular disease. AI can facilitate every stage of the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. AREAS COVERED: In this paper, we review state-of-the-art AI techniques and their current applications in cardiac imaging, and discuss the future role of AI as a precision medicine tool. EXPERT OPINION: Cardiovascular medicine is primed for scalable AI applications which can interpret vast amounts of clinical and imaging data in greater depth than ever before. AI-augmented medical systems have the potential to improve workflow and provide reproducible and objective quantitative results which can inform clinical decisions. In the foreseeable future, AI may work in the background of cardiac image analysis software and routine clinical reporting, automatically collecting data and enabling real-time diagnosis and risk stratification.

Entities:  

Keywords:  Artificial intelligence; cardiovascular imaging; deep learning; machine learning; precision medicine; risk stratification

Mesh:

Year:  2020        PMID: 32510252      PMCID: PMC7382901          DOI: 10.1080/17434440.2020.1777855

Source DB:  PubMed          Journal:  Expert Rev Med Devices        ISSN: 1743-4440            Impact factor:   3.166


  95 in total

1.  A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography.

Authors:  Majd Zreik; Robbert W van Hamersvelt; Jelmer M Wolterink; Tim Leiner; Max A Viergever; Ivana Isgum
Journal:  IEEE Trans Med Imaging       Date:  2018-11-28       Impact factor: 10.048

2.  Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features.

Authors:  Yefeng Zheng; Adrian Barbu; Bogdan Georgescu; Michael Scheuering; Dorin Comaniciu
Journal:  IEEE Trans Med Imaging       Date:  2008-11       Impact factor: 10.048

3.  Machine learning based automated dynamic quantification of left heart chamber volumes.

Authors:  Akhil Narang; Victor Mor-Avi; Aldo Prado; Valentina Volpato; David Prater; Gloria Tamborini; Laura Fusini; Mauro Pepi; Neha Goyal; Karima Addetia; Alexandra Gonçalves; Amit R Patel; Roberto M Lang
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2019-05-01       Impact factor: 6.875

4.  Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass.

Authors:  Valentina Volpato; Victor Mor-Avi; Akhil Narang; David Prater; Alexandra Gonçalves; Gloria Tamborini; Laura Fusini; Mauro Pepi; Amit R Patel; Roberto M Lang
Journal:  Echocardiography       Date:  2018-12-28       Impact factor: 1.724

5.  Pericardial fat burden on ECG-gated noncontrast CT in asymptomatic patients who subsequently experience adverse cardiovascular events.

Authors:  Victor Y Cheng; Damini Dey; Balaji Tamarappoo; Ryo Nakazato; Heidi Gransar; Romalisa Miranda-Peats; Amit Ramesh; Nathan D Wong; Leslee J Shaw; Piotr J Slomka; Daniel S Berman
Journal:  JACC Cardiovasc Imaging       Date:  2010-04

6.  Phenotypic Clustering of Left Ventricular Diastolic Function Parameters: Patterns and Prognostic Relevance.

Authors:  Megan Cummins Lancaster; Alaa Mabrouk Salem Omar; Sukrit Narula; Hemant Kulkarni; Jagat Narula; Partho P Sengupta
Journal:  JACC Cardiovasc Imaging       Date:  2018-04-18

7.  Dermatologist-level classification of skin cancer with deep neural networks.

Authors:  Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal:  Nature       Date:  2017-01-25       Impact factor: 49.962

8.  Machine Learning methods for Quantitative Radiomic Biomarkers.

Authors:  Chintan Parmar; Patrick Grossmann; Johan Bussink; Philippe Lambin; Hugo J W L Aerts
Journal:  Sci Rep       Date:  2015-08-17       Impact factor: 4.379

9.  Fast and accurate view classification of echocardiograms using deep learning.

Authors:  Ali Madani; Ramy Arnaout; Mohammad Mofrad; Rima Arnaout
Journal:  NPJ Digit Med       Date:  2018-03-21

10.  Radiomics versus Visual and Histogram-based Assessment to Identify Atheromatous Lesions at Coronary CT Angiography: An ex Vivo Study.

Authors:  Márton Kolossváry; Júlia Karády; Yasuka Kikuchi; Alexander Ivanov; Christopher L Schlett; Michael T Lu; Borek Foldyna; Béla Merkely; Hugo J Aerts; Udo Hoffmann; Pál Maurovich-Horvat
Journal:  Radiology       Date:  2019-08-06       Impact factor: 11.105

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  6 in total

Review 1.  Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease.

Authors:  Andrew Lin; Márton Kolossváry; Manish Motwani; Ivana Išgum; Pál Maurovich-Horvat; Piotr J Slomka; Damini Dey
Journal:  Radiol Cardiothorac Imaging       Date:  2021-02-25

Review 2.  Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools.

Authors:  Demilade A Adedinsewo; Amy W Pollak; Sabrina D Phillips; Taryn L Smith; Anna Svatikova; Sharonne N Hayes; Sharon L Mulvagh; Colleen Norris; Veronique L Roger; Peter A Noseworthy; Xiaoxi Yao; Rickey E Carter
Journal:  Circ Res       Date:  2022-02-17       Impact factor: 23.213

3.  CT-based radiomics and machine learning for the prediction of myocardial ischemia: Toward increasing quantification.

Authors:  Andrew Lin; Damini Dey
Journal:  J Nucl Cardiol       Date:  2020-07-16       Impact factor: 3.872

Review 4.  Artificial intelligence in cardiovascular CT: Current status and future implications.

Authors:  Andrew Lin; Márton Kolossváry; Manish Motwani; Ivana Išgum; Pál Maurovich-Horvat; Piotr J Slomka; Damini Dey
Journal:  J Cardiovasc Comput Tomogr       Date:  2021-03-22

5.  Estimation of Nuclear Medicine Exposure Measures Based on Intelligent Computer Processing.

Authors:  Junfeng Wang; Fangxiao Wang; Yue Liu; Yuanfan Xu; Jiangtao Liang; Ziming Su
Journal:  J Healthc Eng       Date:  2021-09-27       Impact factor: 2.682

6.  Radiomic phenotype of epicardial adipose tissue in the prognosis of atrial fibrillation recurrence after catheter ablation in patients with lone atrial fibrillation.

Authors:  Julia Ilyushenkova; Svetlana Sazonova; Evgeny Popov; Konstantin Zavadovsky; Roman Batalov; Evgeny Archakov; Tatyana Moskovskih; Sergey Popov; Stanislav Minin; Alexander Romanov
Journal:  J Arrhythm       Date:  2022-08-16
  6 in total

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