Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Subhi J. Al'Aref; Khalil Anchouche; Gurpreet Singh; Piotr J. Slomka; Kranthi K. Kolli; Amit Kumar; Mohit Pandey; Gabriel Maliakal; Alexander R. Van Rosendael; Ashley N. Beecy; Daniel S. Berman; Jonathan Leipsic; Koen Nieman; Daniele Andreini; Gianluca Pontone; U. Joseph Schoepf; Leslee J. Shaw; Hyuk Jae Chang; Jagat Narula; Jeroen J. Bax; Yuanfang Guan; James K. Min

doi:10.1093/eurheartj/ehy404

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Subhi J. Al'Aref, Khalil Anchouche, Gurpreet Singh, Piotr J. Slomka, Kranthi K. Kolli, Amit Kumar, Mohit Pandey, Gabriel Maliakal, Alexander R. Van Rosendael, Ashley N. Beecy, Daniel S. Berman, Jonathan Leipsic, Koen Nieman, Daniele Andreini, Gianluca Pontone, U. Joseph Schoepf, Leslee J. Shaw, Hyuk Jae Chang, Jagat Narula, Jeroen J. BaxYuanfang Guan, James K. Min

Department of Internal Medicine

Research output: Contribution to journal › Review article › peer-review

119 Citations (Scopus)

Abstract

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.

Original language	English
Pages (from-to)	1975-1986
Number of pages	12
Journal	European heart journal
Volume	40
Issue number	24
DOIs	https://doi.org/10.1093/eurheartj/ehy404
Publication status	Published - 2019 Jun 21

Bibliographical note

Publisher Copyright:
©The Author(s) 2019.

All Science Journal Classification (ASJC) codes

Cardiology and Cardiovascular Medicine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1093/eurheartj/ehy404

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Al'Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., Pandey, M., Maliakal, G., Van Rosendael, A. R., Beecy, A. N., Berman, D. S., Leipsic, J., Nieman, K., Andreini, D., Pontone, G., Schoepf, U. J., Shaw, L. J., Chang, H. J., Narula, J., ... Min, J. K. (2019). Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European heart journal, 40(24), 1975-1986. https://doi.org/10.1093/eurheartj/ehy404

Al'Aref, Subhi J. ; Anchouche, Khalil ; Singh, Gurpreet ; Slomka, Piotr J. ; Kolli, Kranthi K. ; Kumar, Amit ; Pandey, Mohit ; Maliakal, Gabriel ; Van Rosendael, Alexander R. ; Beecy, Ashley N. ; Berman, Daniel S. ; Leipsic, Jonathan ; Nieman, Koen ; Andreini, Daniele ; Pontone, Gianluca ; Schoepf, U. Joseph ; Shaw, Leslee J. ; Chang, Hyuk Jae ; Narula, Jagat ; Bax, Jeroen J. ; Guan, Yuanfang ; Min, James K. / Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. In: European heart journal. 2019 ; Vol. 40, No. 24. pp. 1975-1986.

@article{e16ce90946d343d88d03b8d177fba3e8,

title = "Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging",

abstract = "Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.",

author = "Al'Aref, {Subhi J.} and Khalil Anchouche and Gurpreet Singh and Slomka, {Piotr J.} and Kolli, {Kranthi K.} and Amit Kumar and Mohit Pandey and Gabriel Maliakal and {Van Rosendael}, {Alexander R.} and Beecy, {Ashley N.} and Berman, {Daniel S.} and Jonathan Leipsic and Koen Nieman and Daniele Andreini and Gianluca Pontone and Schoepf, {U. Joseph} and Shaw, {Leslee J.} and Chang, {Hyuk Jae} and Jagat Narula and Bax, {Jeroen J.} and Yuanfang Guan and Min, {James K.}",

note = "Publisher Copyright: {\textcopyright}The Author(s) 2019.",

year = "2019",

month = jun,

day = "21",

doi = "10.1093/eurheartj/ehy404",

language = "English",

volume = "40",

pages = "1975--1986",

journal = "European Heart Journal",

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Al'Aref, SJ, Anchouche, K, Singh, G, Slomka, PJ, Kolli, KK, Kumar, A, Pandey, M, Maliakal, G, Van Rosendael, AR, Beecy, AN, Berman, DS, Leipsic, J, Nieman, K, Andreini, D, Pontone, G, Schoepf, UJ, Shaw, LJ, Chang, HJ, Narula, J, Bax, JJ, Guan, Y & Min, JK 2019, 'Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging', European heart journal, vol. 40, no. 24, pp. 1975-1986. https://doi.org/10.1093/eurheartj/ehy404

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. / Al'Aref, Subhi J.; Anchouche, Khalil; Singh, Gurpreet; Slomka, Piotr J.; Kolli, Kranthi K.; Kumar, Amit; Pandey, Mohit; Maliakal, Gabriel; Van Rosendael, Alexander R.; Beecy, Ashley N.; Berman, Daniel S.; Leipsic, Jonathan; Nieman, Koen; Andreini, Daniele; Pontone, Gianluca; Schoepf, U. Joseph; Shaw, Leslee J.; Chang, Hyuk Jae; Narula, Jagat; Bax, Jeroen J.; Guan, Yuanfang; Min, James K.

In: European heart journal, Vol. 40, No. 24, 21.06.2019, p. 1975-1986.

Research output: Contribution to journal › Review article › peer-review

TY - JOUR

T1 - Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

AU - Al'Aref, Subhi J.

AU - Anchouche, Khalil

AU - Singh, Gurpreet

AU - Slomka, Piotr J.

AU - Kolli, Kranthi K.

AU - Kumar, Amit

AU - Pandey, Mohit

AU - Maliakal, Gabriel

AU - Van Rosendael, Alexander R.

AU - Beecy, Ashley N.

AU - Berman, Daniel S.

AU - Leipsic, Jonathan

AU - Nieman, Koen

AU - Andreini, Daniele

AU - Pontone, Gianluca

AU - Schoepf, U. Joseph

AU - Shaw, Leslee J.

AU - Chang, Hyuk Jae

AU - Narula, Jagat

AU - Bax, Jeroen J.

AU - Guan, Yuanfang

AU - Min, James K.

PY - 2019/6/21

Y1 - 2019/6/21

N2 - Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.

AB - Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.

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U2 - 10.1093/eurheartj/ehy404

DO - 10.1093/eurheartj/ehy404

M3 - Review article

C2 - 30060039

AN - SCOPUS:85068474187

VL - 40

SP - 1975

EP - 1986

JO - European Heart Journal

JF - European Heart Journal

SN - 0195-668X

IS - 24

ER -

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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