Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease

被引：55

作者：

Lin, Andrew ^{[1
]}

Kolossvary, Marton ^{[3
]}

Motwani, Manish ^{[5
]}

Isgum, Ivana ^{[6
,7
]}

Maurovich-Horvat, Pal ^{[3
,4
]}

Slomka, Piotr J. ^{[2
]}

Dey, Damini ^{[1
]}

机构：

[1] Cedars Sinai Med Ctr, Biomed Imaging Res Inst, 116 N Robertson Blvd, Los Angeles, CA 90048 USA

[2] Cedars Sinai Med Ctr, Artificial Intelligence Med Program, 116 N Robertson Blvd, Los Angeles, CA 90048 USA

[3] Semmelweis Univ, Heart & Vasc Ctr, MTA SE Cardiovasc Imaging Res Grp, Budapest, Hungary

[4] Semmelweis Univ, Med Imaging Ctr, Budapest, Hungary

[5] Manchester Univ Hosp NHS Fdn Trust, Manchester Heart Ctr, Manchester, England

[6] Amsterdam Univ Med Ctr, Dept Biomed Engn & Phys, Dept Radiol & Nucl Med, Amsterdam, Netherlands

[7] Amsterdam Univ Med Ctr, Amsterdam Cardiovasc Sci, Amsterdam, Netherlands

来源：

RADIOLOGY-CARDIOTHORACIC IMAGING | 2021年 / 3卷 / 01期

关键词：

COMPUTED-TOMOGRAPHY ANGIOGRAPHY; MYOCARDIAL-PERFUSION SPECT; FRACTIONAL FLOW RESERVE; ALL-CAUSE MORTALITY; NAPKIN-RING SIGN; CT ANGIOGRAPHY; CARDIAC CT; CALCIUM; PREDICTION; QUANTIFICATION;

D O I：

10.1148/ryct.2021200512

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Artificial intelligence (AI) describes the use of computational techniques to perform tasks that normally require human cognition. Machine learning and deep learning are subfields of AI that are increasingly being applied to cardiovascular imaging for risk stratifica-tion. Deep learning algorithms can accurately quantify prognostic biomarkers from image data. Additionally, conventional or AI-based imaging parameters can be combined with clinical data using machine learning models for individualized risk prediction. The aim of this review is to provide a comprehensive review of state-of-the-art AI applications across various noninvasive imaging modalities (coronary artery calcium scoring CT, coronary CT angiography, and nuclear myocardial perfusion imaging) for the quantification of cardiovascular risk in coronary artery disease.(c) RSNA, 2021

引用

页数：13

共 58 条

[1] QUANTIFICATION OF CORONARY-ARTERY CALCIUM USING ULTRAFAST COMPUTED-TOMOGRAPHY [J].

AGATSTON, AS ;

JANOWITZ, WR ;

HILDNER, FJ ;

ZUSMER, NR ;

VIAMONTE, M ;

DETRANO, R .

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 1990, 15 (04) :827-832

[2] A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA [J].

Al'Aref, Subhi J. ;

Singh, Gurpreet ;

Choi, Jeong W. ;

Xu, Zhuoran ;

Maliakal, Gabriel ;

van Rosendael, Alexander R. ;

Lee, Benjamin C. ;

Fatima, Zahra ;

Andreini, Daniele ;

Bax, Jeroen J. ;

Cademartiri, Filippo ;

Chinnaiyan, Kavitha ;

Chow, Benjamin J. W. ;

Conte, Edoardo ;

Cury, Ricardo C. ;

Feuchtner, Gudruf ;

Hadamitzky, Martin ;

Kim, Yong-Jin ;

Lee, Sang-Eun ;

Leipsic, Jonathon A. ;

Maffei, Erica ;

Marques, Hugo ;

Plank, Fabian ;

Pontone, Gianluca ;

Raff, Gilbert L. ;

Villines, Todd C. ;

Weirich, Harald G. ;

Cho, Iksung ;

Danad, Ibrahim ;

Han, Donghee ;

Heo, Ran ;

Lee, Ji Hyun ;

Rizvi, Asim ;

Stuijfzand, Wijnand J. ;

Gransar, Heidi ;

Lu, Yao ;

Sung, Ji Min ;

Park, Hyung-Bok ;

Berman, Daniel S. ;

Budoff, Matthew J. ;

Samady, Habib ;

Stone, Peter H. ;

Virmani, Renu ;

Narula, Jagat ;

Chang, Hyuk-Jae ;

Lin, Fay Y. ;

Baskaran, Lohendran ;

Shaw, Leslee J. ;

Min, James K. .

JACC-CARDIOVASCULAR IMAGING, 2020, 13 (10) :2162-2173

[3] Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry [J].

Al'Arefilb, Subhi J. ;

Maliakal, Gabriel ;

Singh, Gurpreet ;

van Rosendael, Alexander R. ;

Ma, Xiaoyue ;

Xu, Zhuoran ;

Alawamlh, Omar Al Hussein ;

Lee, Benjamin ;

Pandey, Mohit ;

Achenbach, Stephan ;

Al-Mallah, Mouaz H. ;

Andreini, Daniele ;

Bax, Jeroen J. ;

Berman, Daniel S. ;

Budoff, Matthew J. ;

Cademartiri, Filippo ;

Canister, Tracy Q. ;

Chang, Hyuk-Jae ;

Chinnaiyan, Kavitha ;

Chow, Benjamin J. W. ;

Cury, Ricardo C. ;

DeLago, Augustin ;

Feuchtner, Gudrun ;

Hadamitzky, Martin ;

Hausleiter, Joerg ;

Kaufmann, Philipp A. ;

Kim, Yong-Jin ;

Leipsic, Jonathon A. ;

Maffei, Erica ;

Marques, Hugo ;

Goncalves, Pedro de Araujo ;

Pontone, Gianluca ;

Raff, Gilbert L. ;

Rubinshtein, Ronen ;

Villines, Todd C. ;

Gransar, Heidi ;

Lu, Yao ;

Jones, Erica C. ;

Pena, Jessica M. ;

Lin, Fay Y. ;

Min, James K. ;

Shaw, Leslee J. .

EUROPEAN HEART JOURNAL, 2020, 41 (03) :359-367

[4] Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning [J].

Alonso, David Haro ;

Wernick, Miles N. ;

Yang, Yongyi ;

Germano, Guido ;

Berman, Daniel S. ;

Slomka, Piotr .

JOURNAL OF NUCLEAR CARDIOLOGY, 2019, 26 (05) :1746-1754

[5] Detecting human coronary inflammation by imaging perivascular fat [J].

Antonopoulos, Alexios S. ;

Sanna, Fabio ;

Sabharwal, Nikant ;

Thomas, Sheena ;

Oikonomou, Evangelos K. ;

Herdman, Laura ;

Margaritis, Marios ;

Shirodaria, Cheerag ;

Kampoli, Anna-Maria ;

Akoumianakis, Ioannis ;

Petrou, Mario ;

Sayeed, Rana ;

Krasopoulos, George ;

Psarros, Constantinos ;

Ciccone, Patricia ;

Brophy, Carl M. ;

Digby, Janet ;

Kelion, Andrew ;

Uberoi, Raman ;

Anthony, Suzan ;

Alexopoulos, Nikolaos ;

Tousoulis, Dimitris ;

Achenbach, Stephan ;

Neubauer, Stefan ;

Channon, Keith M. ;

Antoniades, Charalambos .

SCIENCE TRANSLATIONAL MEDICINE, 2017, 9 (398)

[6] Quantification of Coronary Arterial Stenoses by Multidetector CT Angiography in Comparison With Conventional Angiography: Methods, Caveats, and Implications [J].

Arbab-Zadeh, Armin ;

Hoe, John .

JACC-CARDIOVASCULAR IMAGING, 2011, 4 (02) :191-202

[7] Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population [J].

Arsanjani, Reza ;

Xu, Yuan ;

Dey, Damini ;

Vahistha, Vishal ;

Shalev, Aryeh ;

Nakanishi, Rine ;

Hayes, Sean ;

Fish, Mathews ;

Berman, Daniel ;

Germano, Guido ;

Slomka, Piotr J. .

JOURNAL OF NUCLEAR CARDIOLOGY, 2013, 20 (04) :553-562

[8] Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning [J].

Betancur, Julian ;

Otaki, Yuka ;

Motwani, Manish ;

Fish, Mathews B. ;

Lemley, Mark ;

Dey, Damini ;

Gransar, Heidi ;

Tamarappoo, Balaji ;

Germano, Guido ;

Sharir, Tali ;

Berman, Daniel S. ;

Slomka, Piotr J. .

JACC-CARDIOVASCULAR IMAGING, 2018, 11 (07) :1000-1009

[9] Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study [J].

Betancur, Julian ;

Commandeur, Frederic ;

Motlagh, Mahsaw ;

Sharir, Tali ;

Einstein, Andrew J. ;

Bokhari, Sabahat ;

Fish, Mathews B. ;

Ruddy, Terrence D. ;

Kaufmann, Philipp ;

Sinusas, Albert J. ;

Miller, Edward J. ;

Bateman, Timothy M. ;

Dorbala, Sharmila ;

Di Carli, Marcelo ;

Germano, Guido ;

Otaki, Yuka ;

Tamarappoo, Balaji K. ;

Dey, Damini ;

Berman, Daniel S. ;

Slomka, Piotr J. .

JACC-CARDIOVASCULAR IMAGING, 2018, 11 (11) :1654-1663

[10] Long-term prognosis associated with coronary calcification - Observations from a registry of 25,253 patients [J].

Budoff, Matthew J. ;

Shaw, Leslee J. ;

Liu, Sandy T. ;

Weinstein, Steven R. ;

Mosler, Tristen P. ;

Tseng, Philip H. ;

Flores, Ferdinand R. ;

Callister, Tracy Q. ;

Raggi, Paolo ;

Berman, Daniel S. .

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2007, 49 (18) :1860-1870

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