Swipe om te navigeren naar een ander artikel
Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.
It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician’s perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care.
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5(4006):1–8.
Arsanjani R, Xu Y, Dey D, et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population. J Nucl Cardiol. 2013;20:553–62. CrossRef
Arsanjani R, Dey D, Khachatryan T, et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol. 2015;22:877–84. CrossRef
Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal. 2016;30:108–19. CrossRef
Baessler B, Mannil M, Oebel S, et al. Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images. Radiology. 2018;286(1):103–12. CrossRef
Bai W, Sinclair M, Tarroni G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018;20(65):1–12.
Betancur J, Otaki Y, Motwani M, et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning. JACC Cardiovasc Imaging. 2018;11(7):1000–9. CrossRef
Betancur J, Commandeur F, Motlagh M, et al. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study. JACC Cardiovasc Imaging. 2018;11(11):1654–63. CrossRef
Bratt A, Kim J, Pollie M, et al. Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification. J Cardiovasc Magn Reson. 2019;21(1):1–11. CrossRef
Budoff MJ, Dowe D, Jollis JG, et al. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease. J Am Coll Cardiol. 2008;52(21):1724–32. CrossRef
Buechel RR, Kaufmann PA, Gaemperli O. Single-photon emission computed tomography. In: Nieman K, Gaemperli O, Lancellotti P, Plein S, editors. Advanced cardiac imaging. 1st ed. Sawston, Cambridge: Woodhead; 2015. pp. 47–69. CrossRef
Carneiro G, Nascimento JC. Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Trans Pattern Anal Mach Intell. 2013;35(11):2592–607. CrossRef
Cikes M, Sanchez-Martinez S, Claggett B, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. 2018;21:74–85. CrossRef
Coenen A, Kim YH, Kruk M, et al. 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;11(6):1–11. CrossRef
Corlan AD. Medline Trend: automated yearly statistics of PubMed results for any query [Internet]. 2004. http://dan.corlan.net/medline-trend.html. Accessed 12 Nov 2018.
Dawes TJW, de Marvao A, Shi W, et al. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology. 2017;283(2):381–90. CrossRef
Dey D, Gaur S, Ovrehus KA, et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol. 2018;28:2655–64. CrossRef
Driessen RS, Raijmakers PG, Danad I, et al. Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease. Eur J Nucl Med Mol Imaging. 2018;45(7):1091–100. CrossRef
Editorial. AI Diagnostics need attention. Nature. 2018;555(7696):285
Feigenbaum H. Evolution of echocardiography. Circulation. 1996;93(7):1321–7. CrossRef
Ferreira VM, Robson MD, Karamitsos TD, et al. Magnetic resonance imaging. In: Nieman K, Gaemperli O, Lancellotti P, Plein S, editors. Advanced cardiac imaging. Sawston, Cambridge: Woodhead; 2015. pp. 127–69. CrossRef
Freiman M, Nickisch H, Prevrhal S, et al. Improving CCTA-based lesions’ hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation. Med Phys. 2017;44(3):1040–9. CrossRef
Graff CG, Sidky EY. Compressive sensing in medical imaging. Appl Opt. 2015;54(8):23–44. CrossRef
Han D, Lee JH, Rizvi A, et al. Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: a machine learning approach. J Nucl Cardiol. 2018;25(1):223–33. CrossRef
Hasselberg NE, Edvardsen T. Ultrasound/echocardiography. In: Nieman K, Gaemperli O, Lancellotti P, Plein S, editors. Advanced cardiac imaging. 1st ed. Sawston, Cambridge: Woodhead; 2015. pp. 15–46. CrossRef
Hinton G. Deep learning—a technology with the potential to transform health care opinion. JAMA. 2018;321(11):1101–2. CrossRef
Itu L, Rapaka S, Passerini T, et al. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol. 2016;121:42–52. CrossRef
Karim R, Bhagirath P, Claus P, et al. Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late gadolinium enhancement MR images. Med Image Anal. 2016;30:95–107. CrossRef
Khamis H, Zurakhov G, Azar V, et al. Automatic apical view classification of echocardiograms using a discriminative learning dictionary. Med Image Anal. 2017;36:15–21. CrossRef
Knaapen P, Lubberink M. Positron emission tomography. In: Nieman K, Gaemperli O, Lancellotti P, Plein S, editors. Advanced cardiac imaging. Cambridge: Woodhead Publishing; 2015:71–95.
Knackstedt C, Bekkers SCAM, Schummers G, et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the FAST-EFs Multicenter Study. J Am Coll Cardiol. 2015;66:1456–66. CrossRef
Kolossváry M, Karády J, Szilveszter B, et al. Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign. Circ Cardiovasc Imaging. 2017;10(12):1–9. CrossRef
Krittanawong C, Zhang H, Wang Z, Aydar MKT. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–64. CrossRef
M. Zreik, T. Leiner, B. D. de Vos, et al. Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks. IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague. 2016:40–43.
Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. npj Digit Med. 2018;1(6):1–8.
Mannil M, Von Spiczak J, Manka R, Alkadhi H. Texture analysis and machine learning for detecting myocardial infarction in noncontrast low-dose computed tomography: unveiling the invisible. Invest Radiol. 2018;53(6):338–43. CrossRef
Medvedofsky D, Addetia K, Hamilton J, et al. Semi-automated echocardiographic quantification of right ventricular size and function. Int J Cardiovasc Imaging. 2015;31:1149–57. CrossRef
Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med. 2018;131:129–33. CrossRef
Minsky M. Why people think computers can’t. AI Mag. 1982;3(4):3–15.
Moghaddasi H, Nourian S. Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos. Comput Biol Med. 2016;73:47–55. CrossRef
Mortazavi BJ, Desai N, Zhang J, et al. Prediction of adverse events in patients undergoing major cardiovascular procedures. IEEE J Biomed Health Inform. 2017;21(6):1719–29. CrossRef
Motwani M, Dey D, Berman DS, et al. 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;38:500–7.
Nakajima K, Kudo T, Nakata T, et al. 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;44:2280–9. CrossRef
Nakajima K, Okuda K, Watanabe S, et al. Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database. Ann Nucl Med. 2018;32:303–10. CrossRef
Narula S, Shameer K, Salem OAM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68(21):2287–95. CrossRef
Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal. 2017;35:159–71. CrossRef
Nieman K, Coenen A, Dijkshoorn M. Computed tomography. In: Nieman K, Gaemperli O, Lancellotti P, Plein S, editors. Advanced cardiac imaging. 1st ed. Sawston, Cambridge: Woodhead; 2015. pp. 97–125. CrossRef
Papanicolas I, Woskie LR, Jha AK. Health care spending in the United States and other high-income countries. JAMA. 2018;319(10):1024–39. CrossRef
Redekop WK, Mladsi D. The faces of personalized medicine: a framework for understanding its meaning and scope. Value Health. 2013;16:S4–S9. CrossRef
RIVM. Trend in aantallen verrichting [Internet]. Diagnostiek. 2018. https://www.rivm.nl/medische-stralingstoepassingen/trends-en-stand-van-zaken/diagnostiek#Trend in aantallen verrichtingen. Accessed 22 Nov 2018.
van Rosendael AR, Maliakal G, Kolli KK, et al. 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;12(3):204–9. CrossRef
Rumsfeld JS, Joynt KE, Maddox TM. Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol. 2016;13:350–9. CrossRef
Russell S, Norvig P, editors. Introduction. In: Artificial intelligence: a modern approach. 3rd ed. Malaysia: Pearson Education Limited; 2016. pp. 1–30.
Samad MD, Wehner GJ, Arbabshirani MR, et al. Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning. Eur Heart J Cardiovasc Imaging. 2018;19(7):730–8. CrossRef
Sengupta PP, Huang YM, Bansal M, et al. Cognitive machine-learning algorithm for cardiac imaging; a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging. 2016;9:1–10. CrossRef
Shah SJ, Katz DH, Selvaraj S, et al. Heart failure phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131:269–79. CrossRef
Snaauw G, Gong D., Maicas G. et al. End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy. 2019:802–5.
Sudarshan V, Ng EY, Acharya UR, et al. Computer-aided diagnosis of myocardial infarction using ultrasound images with DWT, GLCM and HOS methods: a comparative study. Comput Biol Med. 2015;62:86–93. CrossRef
Suinesiaputra A, Sanghvi MM, Aung N, et al. Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results. Int J Cardiovasc Imaging. 2018;34:281–91. CrossRef
Tabassian M, Alessandrini M, Herbots L, et al. Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification. Int J Cardiovasc Imaging. 2017;33:1159–67. CrossRef
Tabassian M, Sunderji I, Erdei T, et al. Diagnosis of heart failure with preserved ejection fraction: machine learning of spatiotemporal variations in left ventricular deformation. J Am Soc Echocardiogr. 2018;31:1272–1284. CrossRef
Tamborini G, Piazzese C, Lang RM, et al. Feasibility and Accuracy of automated software for transthoracic three-dimensional left ventricular volume and function analysis: comparisons with two-dimensional echocardiography, three-dimensional transthoracic manual method, and cardiac magnetic resonance imaging. J Am Soc Echocardiogr. 2017;30(11):1049–58. CrossRef
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;39:78–86. CrossRef
Tesche C, Vliegenthart R, Duguay TM, et al. Coronary computed tomographic angiography-derived fractional flow reserve for therapeutic decision making. Am J Cardiol. 2017;120:2121–7. CrossRef
The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2016;37:2129–200. CrossRef
U.S. Food and Drug Administration. Digital health innovation action plan. 2017.
Wagholikar KB, Fischer CM, Goodson A, et al. Extraction of ejection fraction from echocardiography notes for constructing a cohort of patients having heart failure with reduced ejection fraction (HFrEF). J Med Syst. 2018;42(209):1–12.
Wolterink JM, Leiner T, De Vos BD, et al. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal. 2016;34:123–36. CrossRef
Wolterink JM, Leiner T, Takx RAP, Viergever MA, Išgum I. Cardiac CT with ambiguity detection. IEEE Trans Med Imaging. 2015;34(9):1867–78. CrossRef
Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging. 2017;36(12):2536–45. CrossRef
Zhang J, Gajjala S, Agrawal P, et al. Fully automated echocardiogram interpretation in clinical practice. Circulation. 2018;138:1623–35. CrossRef
Zheng Q, Delingette H, Duchateau N, Ayache N. 3‑D consistent and robust segmentation of cardiac images by deep learning with spatial propagation. IEEE Trans Med Imaging. 2018;37(9):2137–48. CrossRef
Zreik M, Lessmann N, van Hamersvelt RW, et al. Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis. Med Image Anal. 2018;44:72–85. CrossRef
M. Zreik, R. W. van Hamersvelt, J. M. Wolterink et al. A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography. IEEE Transactions on Medical Imaging. 2019;(38)7:1588–98. CrossRef
- Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist
K. R. Siegersma
D. P. Chew
J. W. Verjans
- Bohn Stafleu van Loghum