Skip to main content
Have a personal or library account? Click to login
Artificial Intelligence in Cardiovascular Imaging Cover

Artificial Intelligence in Cardiovascular Imaging

Open Access
|Apr 2020

References

  1. Russell SJ Norvig P Artificial intelligence: a modern approach 2nd ed Upper Saddle River, N.J. Prentice Hall/Pearson Education 2003
  2. Darcy AM Louie AK Roberts LW Machine Learning and the Profession of Medicine JAMA 2016 Feb 9 315 6 551 2
  3. Papanicolas I Woskie LR Jha AK Health Care Spending in the United States and Other High-Income Countries JAMA 2018 Mar 13 319 10 1024 39
  4. Dey D Slomka PJ Leeson P Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review J Am Coll Cardiol 2019 Mar 26 73 11 1317 35
  5. Lee JG Jun S Cho YW Deep Learning in Medical Imaging: General Overview Korean J Radiol 2017 Jul–Aug 18 4 570 84
  6. Madani A Arnaout R Mofrad M Arnaout R Fast and accurate view classification of echocardiograms using deep learning NPJ Digit Med 2018 1 6
  7. Zhang J Gajjala S Agrawal P Fully Automated Echocardiogram Interpretation in Clinical Practice Circulation 2018 Oct 16 138 16 1623 35
  8. Tabassian M Alessandrini M Herbots L Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification Int J Cardiovasc Imaging 2017 Aug 33 8 1159 67
  9. Narula S Shameer K Salem Omar AM Dudley JT Sengupta PP Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography J Am Coll Cardiol 2016 Nov 29 68 21 2287 95
  10. Sengupta PP Huang YM Bansal M Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy Circ Cardiovasc Imaging 2016 Jun 9 6 e004330
  11. Kusunose K Abe T Haga A A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images JACC Cardiovasc Imaging 2020 Feb 13 2 Pt 1 374 381
  12. Shah SJ Katz DH Selvaraj S Phenomapping for novel classification of heart failure with preserved ejection fraction Circulation 2015 Jan 20 131 3 269 79
  13. Lancaster MC Salem Omar AM Narula S Kulkarni H Narula J Sengupta PP Phenotypic Clustering of Left Ventricular Diastolic Function Parameters: Patterns and Prognostic Relevance JACC Cardiovasc Imaging 2019 Jul 12 7 Pt 1 1149 61
  14. Samad MD Ulloa A Wehner GJ Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning JACC Cardiovasc Imaging 2019 Apr 12 4 681 9
  15. Kang D Dey D Slomka PJ Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography J Med Imaging (Bellingham) 2015 Jan 2 1 014003
  16. Motwani M Dey D Berman DS Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis Eur Heart J 2017 Feb 14 38 7 500 7
  17. Haro Alonso D Wernick MN Yang Y Germano G Berman DS Slomka P Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning J Nucl Cardiol 2019 Oct 26 5 1746 54
  18. Shameer K Johnson KW Glicksberg BS Dudley JT Sengupta PP Machine learning in cardiovascular medicine: are we there yet? Heart 2018 Jul 104 14 1156 64
  19. Johnson KW Torres Soto J Glicksberg BS Artificial Intelligence in Cardiology J Am Coll Cardiol 2018 Jun 12 71 23 2668 79
  20. Krittanawong C Tunhasiriwet A Zhang H Wang Z Aydar M Kitai T Deep Learning With Unsupervised Feature in Echocardiographic Imaging J Am Coll Cardiol 2017 Apr 25 69 16 2100 1
  21. Feigenbaum H Evolution of echocardiography Circulation 1996 Apr 1 93 7 1321 7
  22. Moghaddasi H Nourian S Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos Comput Biol Med 2016 Jun 1 73 47 55
  23. Ponikowski P Voors AA Anker SD 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC Eur Heart J 2016 Jul 14 37 27 2129 200
  24. Tabassian M Sunderji I Erdei T Diagnosis of Heart Failure With Preserved Ejection Fraction: Machine Learning of Spatiotemporal Variations in Left Ventricular Deformation J Am Soc Echocardiogr 2018 Dec 31 12 1272 84.e9
  25. Wolterink JM Leiner T Viergever MA Isgum I Generative Adversarial Networks for Noise Reduction in Low-Dose CT IEEE Trans Med Imaging 2017 Dec 36 12 2536 45
  26. Wolterink JM Leiner T de Vos BD van Hamersvelt RW Viergever MA Isgum I Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks Med Image Anal 2016 Dec 34 123 36
  27. Zreik M Leiner T de Vos BD van Hamersvelt RW Viergever MA Isgum I Automatic Segmentation of the Left Ventricle in Cardiac Ct Angiography Using Convolutional Neural Networks I S Biomed Imaging 2016 40 43
  28. Itu L Rapaka S Passerini T A machine-learning approach for computation of fractional flow reserve from coronary computed tomography J Appl Physiol (1985) 2016 Jul 1 121 1 42 52
  29. Dey D Gaur S Ovrehus KA Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study Eur Radiol 2018 Jun 28 6 2655 64
  30. Zreik M Lessmann N van Hamersvelt RW Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis Med Image Anal 2018 Feb 44 72 85
  31. Coenen A Kim YH Kruk M Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium Circ Cardiovasc Imaging 2018 Jun 11 6 e007217
  32. Tesche C Vliegenthart R Duguay TM Coronary Computed Tomo-graphic Angiography-Derived Fractional Flow Reserve for Therapeutic Decision Making Am J Cardiol 2017 Dec 15 120 12 2121 7
  33. Siegersma KR Leiner T Chew DP Appelman Y Hofstra L Verjans JW Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist Neth Heart J 2019 Sep 27 9 403 13
  34. van Rosendael AR Maliakal G Kolli KK Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry J Cardiovasc Comput Tomogr 2018 May – Jun 12 3 204 9
  35. Winther HB Hundt C Schmidt B ν-net: Deep Learning for Generalized Biventricular Mass and Function Parameters Using Multicenter Cardiac MRI Data JACC Cardiovasc Imaging 2018 Jul 11 7 1036 8
  36. Tan LK Liew YM Lim E McLaughlin RA Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences Med Image Anal 2017 Jul 39 78 86
  37. Baessler B Mannil M Oebel S Maintz D Alkadhi H Manka R Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images Radiology 2018 Jan 286 1 103 112
  38. Dawes TJW de Marvao A Shi W Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study Radiology 2017 May 283 2 381 90
  39. Samad MD Wehner GJ Arbabshirani MR Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning Eur Heart J Cardiovasc Imaging 2018 Jul 1 19 7 730 8
  40. Driessen RS Raijmakers PG Danad I Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease Eur J Nucl Med Mol Imaging 2018 Jul 45 7 1091 1100
  41. Nakajima K Kudo T Nakata T Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study Eur J Nucl Med Mol Imaging 2017 Dec 44 13 2280 9
  42. Arsanjani R Xu Y Dey D Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population J Nucl Cardiol 2013 Aug 20 4 553 62
  43. Betancur J Commandeur F Motlagh M Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study JACC Cardiovasc Imaging 2018 Nov 11 11 1654 63
  44. Arsanjani R Dey D Khachatryan T Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population J Nucl Cardiol 2015 Oct 22 5 877 84
  45. Betancur J Otaki Y Motwani M Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning JACC Cardiovasc Imaging 2018 Jul 11 7 1000 9
Language: English
Page range: 138 - 145
Published on: Apr 1, 2020
Published by: Houston Methodist DeBakey Heart & Vascular Center
In partnership with: Paradigm Publishing Services

© 2020 Lisa J. Lim, Geoffrey H. Tison, Francesca N. Delling, published by Houston Methodist DeBakey Heart & Vascular Center
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.