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Artificial Intelligence and Machine Learning in Cardiovascular Imaging Cover

Artificial Intelligence and Machine Learning in Cardiovascular Imaging

Methodist DeBakey Cardiovascular Journal
Volume 16 (2020): Issue 4
By:  and    
Open Access
|Oct 2020
References

References

  1. Seetharam K , Kagiyama N , Sengupta PP . Application of mobile health, tele-medicine and artificial intelligence to echocardiography. Echo Res Pract. 2019 Jun 1; 6( 2): R41– R52.
    Search in Google ScholarBack to article
  2. Al'Aref SJ , Anchouche K , Singh G , . Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. 2019 Jun 21; 40( 24): 1975– 86.
    Search in Google ScholarBack to article
  3. Seetharam K , Shrestha S , Sengupta PP . Artificial Intelligence in Cardiovascular Medicine. Curr Treat Options Cardiovasc Med. 2019 May 14; 21( 6): 25.
    Search in Google ScholarBack to article
  4. Seetharam K , Shrestha S , Sengupta PP . Artificial Intelligence in Cardiac Imaging. US Cardiology Review. 2019 Nov; 13( 2): 110– 6.
    Search in Google ScholarBack to article
  5. Johnson KW , Torres Soto J , Glicksberg BS , . Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018 Jun 12; 71( 23): 2668– 79.
    Search in Google ScholarBack to article
  6. Al'Aref SJ Min JK . Cardiac CT: current practice and emerging applications. Heart. 2019 Oct; 105( 20): 1597– 605.
    Search in Google ScholarBack to article
  7. Seetharam K , Shrestha S , Mills JD , Sengupta PP . Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication. Current Cardiovascular Imaging Reports. 2019 May 1; 12( 5). DOI: 10.1007/s12410-019-9490-8.
    Open DOISearch in Google ScholarBack to article
  8. Sengupta PP Adjeroh DA . Will Artificial Intelligence Replace the Human Echocardiographer? Circulation. 2018 Oct 16; 138( 16): 1639– 42.
    Search in Google ScholarBack to article
  9. 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.
    Search in Google ScholarBack to article
  10. Krittanawong C , Zhang H , Wang Z , Aydar M , Kitai T . Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017 May 30; 69( 21): 2657– 64.
    Search in Google ScholarBack to article
  11. Benjamin EJ , Virani SS , Callaway CW , . Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association. Circulation. 2018 Mar 20; 137( 12): e67– e492.
    Search in Google ScholarBack to article
  12. Shrestha S Sengupta PP . The Mechanics of Machine Learning: From a Concept to Value. J Am Soc Echocardiogr. 2018 Dec; 31( 12): 1285– 7.
    Search in Google ScholarBack to article
  13. Krittanawong C , Johnson KW , Rosenson RS , . Deep learning for cardiovascular medicine: a practical primer. Eur Heart J. 2019 Jul 1; 40( 25): 2058– 73.
    Search in Google ScholarBack to article
  14. Bizopoulos P Koutsouris D . Deep Learning in Cardiology. IEEE Rev Biomed Eng. 2019; 12: 168– 93.
    Search in Google ScholarBack to article
  15. Seetharam K , Sengupta PP , Bianco CM . Cardiac mechanics in heart failure with preserved ejection fraction. Echocardiography. 2020 Jun 28.
    Search in Google ScholarBack to article
  16. Seetharam K , Raina S , Sengupta PP . The Role of Artificial Intelligence in Echocardiography. Curr Cardiol Rep. 2020 Jul 30; 22( 9): 99.
    Search in Google ScholarBack to article
  17. Min JK . Chess and Coronary Artery Ischemia: Clinical Implications of Machine-Learning Applications. Circ Cardiovasc Imaging. 2018 Jun; 11( 6): e007943.
    Search in Google ScholarBack to article
  18. Kulina R , Seetharam K , Agarwal S , . Beamforming algorithms for endocardial border detection. Echocardiography. 2018 Oct; 35( 10): 1499– 506.
    Search in Google ScholarBack to article
  19. Seetharam K Lerakis S . Cardiac magnetic resonance imaging: the future is bright. F1000Res. 2019 Sep 13; 8: F1000 Faculty Rev-1636.
    Search in Google ScholarBack to article
  20. 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.
    Search in Google ScholarBack to article
  21. Khamis H , Zurakhov G , Azar V , Raz A , Friedman Z , Adam D . Automatic apical view classification of echocardiograms using a discriminative learning dictionary. Med Image Anal. 2017 Feb; 36: 15– 21.
    Search in Google ScholarBack to article
  22. Knackstedt C , Bekkers SC , Schummers G , . Fully Automated Versus Standard Tracking of Left Ventricular Ejection Fraction and Longitudinal Strain: The FAST-EFs Multicenter Study. J Am Coll Cardiol. 2015 Sep 29; 66( 13): 1456– 66.
    Search in Google ScholarBack to article
  23. 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.
    Search in Google ScholarBack to article
  24. 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.
    Search in Google ScholarBack to article
  25. Nolan MT Thavendiranathan P . Automated Quantification in Echocardiography. JACC Cardiovasc Imaging. 2019 Jun; 12( 6): 1073– 92.
    Search in Google ScholarBack to article
  26. Medvedofsky D , Mor-Avi V , Amzulescu M , . Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study. Eur Heart J Cardiovasc Imaging. 2018 Jan 1; 19( 1): 47– 58.
    Search in Google ScholarBack to article
  27. Tsang W , Salgo IS , Medvedofsky D , . Transthoracic 3D Echocardiographic Left Heart Chamber Quantification Using an Automated Adaptive Analytics Algorithm. JACC Cardiovasc Imaging. 2016 Jul; 9( 7): 769– 82.
    Search in Google ScholarBack to article
  28. Casaclang-Verzosa G , Shrestha S , Khalil MJ , . Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis. JACC Cardiovasc Imaging. 2019 Feb; 12( 2): 236– 48.
    Search in Google ScholarBack to article
  29. Tokodi M , Shrestha S , Bianco C , . Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine. JACC Cardiovasc Imaging. 2020 May; 13( 5): 1119– 32.
    Search in Google ScholarBack to article
  30. Levsky JM , Haramati LB , Spevack DM , . Coronary Computed Tomography Angiography Versus Stress Echocardiography in Acute Chest Pain: A Randomized Controlled Trial. JACC Cardiovasc Imaging. 2018 Sep; 11( 9): 1288– 97.
    Search in Google ScholarBack to article
  31. 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.
    Search in Google ScholarBack to article
  32. Santini G , Della Latta D , Martini N , . An automatic deep learning approach for coronary artery calcium segmentation. In: Eskola H , Väisänen O , Viik J , Hyttinen J , editors. EMBEC & NBC 2017. EMBEC 2017, NBC 2017. IFMBE Proceedings, vol 65. Singapore: Springer; c2017. p. 374– 7.
    Search in Google ScholarBack to article
  33. Baskaran L , Maliakal G , Singh G , . Automatic Segmentation of Cardiovascular Structures Imaged on Cardiac Computed Tomography Angiography using Deep Learning. J Cardiovasc Comput Tomogr. 2019; 13: S9.
    Search in Google ScholarBack to article
  34. Baskaran L , Maliakal G , Al'Aref SJ , . Identification and Quantification of Cardiovascular Structures From CCTA: An End-to-End, Rapid, Pixel-Wise, Deep-Learning Method. JACC Cardiovasc Imaging. 2020 May; 13( 5): 1163– 71.
    Search in Google ScholarBack to article
  35. Han D , Kolli KK , Al'Aref SJ , . Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry. J Am Heart Assoc. 2020 Mar 3; 9( 5): e013958.
    Search in Google ScholarBack to article
  36. Hachamovitch R , Hayes SW , Friedman JD , Cohen I , Berman DS . Stress myocardial perfusion single-photon emission computed tomography is clinically effective and cost effective in risk stratification of patients with a high likelihood of coronary artery disease (CAD) but no known CAD. J Am Coll Cardiol. 2004 Jan 21; 43( 2): 200– 8.
    Search in Google ScholarBack to article
  37. 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.
    Search in Google ScholarBack to article
  38. Betancur J , Hu LH , Commandeur F , . Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study. J Nucl Med. 2019 May; 60( 5): 664– 70.
    Search in Google ScholarBack to article
  39. 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.
    Search in Google ScholarBack to article
  40. 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.
    Search in Google ScholarBack to article
  41. 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.
    Search in Google ScholarBack to article
  42. Tan LK , McLaughlin RA , Lim E , Abdul Aziz YF , Liew YM . Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression. J Magn Reson Imaging. 2018 Jul; 48( 1): 140– 52.
    Search in Google ScholarBack to article
  43. Leng S , Yang X , Zhao X , . Computational Platform Based on Deep Learning for Segmenting Ventricular Endocardium in Long-axis Cardiac MR Imaging. Conf Proc IEEE Eng Med Biol Soc. 2018 Jul; 2018: 4500– 3.
    Search in Google ScholarBack to article
  44. Schoenhagen P Mehta N . Big data, smart computer systems, and doctor-patient relationship. Eur Heart J. 2017 Feb 14; 38( 7): 508– 10.
    Search in Google ScholarBack to article
  45. Zhang J , Gajjala S , Agrawal P , . Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation. 2018 Oct 16; 138( 16): 1623– 35.
    Search in Google ScholarBack to article
  46. Al'Aref SJ , Maliakal G , Singh G , . 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. Eur Heart J. 2020 Jan 14; 41( 3): 359– 67.
    Search in Google ScholarBack to article
  47. Han D , Beecy A , Anchouche K , . Risk Reclassification With Coronary Computed Tomography Angiography-Visualized Nonobstructive Coronary Artery Disease According to 2018 American College of Cardiology/American Heart Association Cholesterol Guidelines (from the Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes : An International Multicenter Registry [CONFIRM]). Am J Cardiol. 2019 Nov 1; 124( 9): 1397– 1405.
    Search in Google ScholarBack to article
  48. Seetharam K , Kagiyama N , Shrestha S , Sengupta P . Clinical Inference From Cardiovascular Imaging: Paradigm Shift Towards Machine-Based Intelligent Platform. Curr Treat Options Cardiovasc Med. 2020 Feb 20; 22( 8).
    Search in Google ScholarBack to article
  49. Shrestha S Sengupta PP . Imaging Heart Failure With Artificial Intelligence: Improving the Realism of Synthetic Wisdom. Circ Cardiovasc Imaging. 2018 Apr; 11( 4): e007723.
    Search in Google ScholarBack to article
  50. Hu LH , Betancur J , Sharir T , . Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry. Eur Heart J Cardiovasc Imaging. 2020 May 1; 21( 5): 549– 59.
    Search in Google ScholarBack to article
  51. Krittanawong C , Johnson KW , Tang WW . How artificial intelligence could redefine clinical trials in cardiovascular medicine: lessons learned from oncology. Per Med. 2019 Mar; 16( 2): 83– 8.
    Search in Google ScholarBack to article
  52. Sengupta PP Shrestha S . Machine Learning for Data-Driven Discovery: The Rise and Relevance. JACC Cardiovasc Imaging. 2019 Apr; 12( 4): 690– 2.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.14797/mdcj-16-4-263 | Journal eISSN: 1947-6108
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Language: English
Page range: 263 - 271
Published on: Oct 1, 2020
Published by: Houston Methodist DeBakey Heart & Vascular Center
In partnership with: Paradigm Publishing Services
Keywords:
artificial intelligence,
machine learning,
deep learning algorithms

© 2020 Karthik Seetharam, James K. Min, published by Houston Methodist DeBakey Heart & Vascular Center
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.

Volume 16 (2020): Issue 4
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