Publication

Machine learning in cardiac CT: Basic concepts and contemporary data

Singh, G., Al'Aref, S. J., Van Assen, M., Kim, T. S., van Rosendael, A., Kolli, K. K., Dwivedi, A., Maliakal, G., Pandey, M., Wang, J., Do, V., Gummalla, M., De Cecco, C. N. & Min, J. K., May-2018, In : Journal of Cardiovascular Computed Tomography. 12, 3, p. 192-201 10 p.

Research output: Contribution to journalReview articleAcademicpeer-review

APA

Singh, G., Al'Aref, S. J., Van Assen, M., Kim, T. S., van Rosendael, A., Kolli, K. K., Dwivedi, A., Maliakal, G., Pandey, M., Wang, J., Do, V., Gummalla, M., De Cecco, C. N., & Min, J. K. (2018). Machine learning in cardiac CT: Basic concepts and contemporary data. Journal of Cardiovascular Computed Tomography, 12(3), 192-201. https://doi.org/10.1016/j.jcct.2018.04.010

Author

Singh, Gurpreet ; Al'Aref, Subhi J. ; Van Assen, Marly ; Kim, Timothy Suyong ; van Rosendael, Alexander ; Kolli, Kranthi K. ; Dwivedi, Aeshita ; Maliakal, Gabriel ; Pandey, Mohit ; Wang, Jing ; Do, Virginie ; Gummalla, Manasa ; De Cecco, Carlo N. ; Min, James K. / Machine learning in cardiac CT : Basic concepts and contemporary data. In: Journal of Cardiovascular Computed Tomography. 2018 ; Vol. 12, No. 3. pp. 192-201.

Harvard

Singh, G, Al'Aref, SJ, Van Assen, M, Kim, TS, van Rosendael, A, Kolli, KK, Dwivedi, A, Maliakal, G, Pandey, M, Wang, J, Do, V, Gummalla, M, De Cecco, CN & Min, JK 2018, 'Machine learning in cardiac CT: Basic concepts and contemporary data', Journal of Cardiovascular Computed Tomography, vol. 12, no. 3, pp. 192-201. https://doi.org/10.1016/j.jcct.2018.04.010

Standard

Machine learning in cardiac CT : Basic concepts and contemporary data. / Singh, Gurpreet; Al'Aref, Subhi J.; Van Assen, Marly; Kim, Timothy Suyong; van Rosendael, Alexander; Kolli, Kranthi K.; Dwivedi, Aeshita; Maliakal, Gabriel; Pandey, Mohit; Wang, Jing; Do, Virginie; Gummalla, Manasa; De Cecco, Carlo N.; Min, James K.

In: Journal of Cardiovascular Computed Tomography, Vol. 12, No. 3, 05.2018, p. 192-201.

Research output: Contribution to journalReview articleAcademicpeer-review

Vancouver

Singh G, Al'Aref SJ, Van Assen M, Kim TS, van Rosendael A, Kolli KK et al. Machine learning in cardiac CT: Basic concepts and contemporary data. Journal of Cardiovascular Computed Tomography. 2018 May;12(3):192-201. https://doi.org/10.1016/j.jcct.2018.04.010


BibTeX

@article{dceb7e724da641018bba0639c925f9eb,
title = "Machine learning in cardiac CT: Basic concepts and contemporary data",
abstract = "Propelled by the synergy of the groundbreaking advancements in the ability to analyze high-dimensional datasets and the increasing availability of imaging and clinical data, machine learning (ML) is poised to transform the practice of cardiovascular medicine. Owing to the growing body of literature validating both the diagnostic performance as well as the prognostic implications of anatomic and physiologic findings, coronary computed tomography angiography (CCTA) is now a well-established non-invasive modality for the assessment of cardiovascular disease. ML has been increasingly utilized to optimize performance as well as extract data from CCTA as well as non-contrast enhanced cardiac CT scans. The purpose of this review is to describe the contemporary state of ML based algorithms applied to cardiac CT, as well as to provide clinicians with an understanding of its benefits and associated limitations.",
keywords = "Machine learning, Computed tomography, Coronary artery calcium, Diagnostic performance, CORONARY-ARTERY-DISEASE, COMPUTED-TOMOGRAPHY ANGIOGRAPHY, FRACTIONAL FLOW RESERVE, CARDIOVASCULAR RISK-ASSESSMENT, AMERICAN-HEART-ASSOCIATION, APPROPRIATE USE CRITERIA, ALL-CAUSE MORTALITY, MYOCARDIAL-PERFUSION, MAGNETIC-RESONANCE, DIAGNOSTIC PERFORMANCE",
author = "Gurpreet Singh and Al'Aref, {Subhi J.} and {Van Assen}, Marly and Kim, {Timothy Suyong} and {van Rosendael}, Alexander and Kolli, {Kranthi K.} and Aeshita Dwivedi and Gabriel Maliakal and Mohit Pandey and Jing Wang and Virginie Do and Manasa Gummalla and {De Cecco}, {Carlo N.} and Min, {James K.}",
year = "2018",
month = may,
doi = "10.1016/j.jcct.2018.04.010",
language = "English",
volume = "12",
pages = "192--201",
journal = "Journal of Cardiovascular Computed Tomography",
issn = "1934-5925",
publisher = "ELSEVIER SCIENCE INC",
number = "3",

}

RIS

TY - JOUR

T1 - Machine learning in cardiac CT

T2 - Basic concepts and contemporary data

AU - Singh, Gurpreet

AU - Al'Aref, Subhi J.

AU - Van Assen, Marly

AU - Kim, Timothy Suyong

AU - van Rosendael, Alexander

AU - Kolli, Kranthi K.

AU - Dwivedi, Aeshita

AU - Maliakal, Gabriel

AU - Pandey, Mohit

AU - Wang, Jing

AU - Do, Virginie

AU - Gummalla, Manasa

AU - De Cecco, Carlo N.

AU - Min, James K.

PY - 2018/5

Y1 - 2018/5

N2 - Propelled by the synergy of the groundbreaking advancements in the ability to analyze high-dimensional datasets and the increasing availability of imaging and clinical data, machine learning (ML) is poised to transform the practice of cardiovascular medicine. Owing to the growing body of literature validating both the diagnostic performance as well as the prognostic implications of anatomic and physiologic findings, coronary computed tomography angiography (CCTA) is now a well-established non-invasive modality for the assessment of cardiovascular disease. ML has been increasingly utilized to optimize performance as well as extract data from CCTA as well as non-contrast enhanced cardiac CT scans. The purpose of this review is to describe the contemporary state of ML based algorithms applied to cardiac CT, as well as to provide clinicians with an understanding of its benefits and associated limitations.

AB - Propelled by the synergy of the groundbreaking advancements in the ability to analyze high-dimensional datasets and the increasing availability of imaging and clinical data, machine learning (ML) is poised to transform the practice of cardiovascular medicine. Owing to the growing body of literature validating both the diagnostic performance as well as the prognostic implications of anatomic and physiologic findings, coronary computed tomography angiography (CCTA) is now a well-established non-invasive modality for the assessment of cardiovascular disease. ML has been increasingly utilized to optimize performance as well as extract data from CCTA as well as non-contrast enhanced cardiac CT scans. The purpose of this review is to describe the contemporary state of ML based algorithms applied to cardiac CT, as well as to provide clinicians with an understanding of its benefits and associated limitations.

KW - Machine learning

KW - Computed tomography

KW - Coronary artery calcium

KW - Diagnostic performance

KW - CORONARY-ARTERY-DISEASE

KW - COMPUTED-TOMOGRAPHY ANGIOGRAPHY

KW - FRACTIONAL FLOW RESERVE

KW - CARDIOVASCULAR RISK-ASSESSMENT

KW - AMERICAN-HEART-ASSOCIATION

KW - APPROPRIATE USE CRITERIA

KW - ALL-CAUSE MORTALITY

KW - MYOCARDIAL-PERFUSION

KW - MAGNETIC-RESONANCE

KW - DIAGNOSTIC PERFORMANCE

U2 - 10.1016/j.jcct.2018.04.010

DO - 10.1016/j.jcct.2018.04.010

M3 - Review article

VL - 12

SP - 192

EP - 201

JO - Journal of Cardiovascular Computed Tomography

JF - Journal of Cardiovascular Computed Tomography

SN - 1934-5925

IS - 3

ER -

ID: 74539092