nach oben

Current Cardiovascular Imaging Reports

Erschienen in:

01.06.2018 | Cardiac Computed Tomography (M Cheezum and B Chow, Section Editors)

Application of Artificial Intelligence in Coronary Computed Tomography Angiography

verfasst von: A. Selvarajah, M. Bennamoun, D. Playford, B. J. W Chow, Girish Dwivedi

Erschienen in: Current Cardiovascular Imaging Reports | Ausgabe 6/2018

Einloggen, um Zugang zu erhalten

Abstract

Purpose of Review

This article summarizes the currently available published literature with regard to the applications of artificial intelligence in cardiac computed tomography angiography.

Recent Findings

Recent studies and emerging data demonstrate feasibility of artificial intelligence-based high-level image analysis and interpretation tools that will likely enable medical practitioners to achieve more accurate diagnosis of coronary artery disease. Emerging artificial intelligence-based computational modeling methods will assist with pre-operative planning for valve disease. Finally, early but significant work is also being performed in relation to real-time assessment of myocardial perfusion and fractional flow reserve using machine learning.

Summary

We anticipate that within the next 5 years, the level of artificial intelligence-driven automation for the analysis and interpretation of cardiac computed tomography angiography will be significantly higher than what is available today. It is also expected that the most productive applications of artificial intelligence in cardiac computed tomography angiography will involve deep learning, utilizing a combination of imaging data and adjunctive data mining to generate more accurate and personalized diagnoses and risk metrics.

Stuart R, Norvig P. Acting rationally: the rational agent approach, In: Artificial intelligence: a modern approach. 2nd ed. Upper Saddle River: Prentice Hall; 2003.

https://www.recode.net/2018/1/11/16859194/silicon-valley-google-youtube-tv-show-future-of-work-msnbc-nbc. Accessed 11th Jan 2018.

https://hbr.org/2016/11/the-simple-economics-of-machine-intelligence (17/11/2016). Extract from: Prediction machines: the simple economics of artificial intelligence, Harvard Business School Press, 2018.

Nixon MS, Aguado AS. Feature extraction & image processing for computer vision. Cambridge: Elsevier: Academic Press; 2012.

• Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37:505–15. This review summarizes the recent applications and developments in machine learning methods with regard to medical imaging.CrossRefPubMed

Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10:257–73.CrossRefPubMed

•• LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature. 2015;521:436–44. A seminal review on deep learning.CrossRefPubMed

Tong S, Chang E. Support vector machine active learning for image retrieval. Proceedings of the 9th ACM International Conference on Multimedia. 2001;107–118.

Slomka PJ, Dey D, Sitek A, Motwani M, Berman DS, Germano G. Cardiac imaging: working towards fully-automated machine analysis & interpretation. Expert Rev Med Devices. 2017;14:197–212.CrossRefPubMedPubMedCentral

10.

Suzuki K, Horiba I, Sugie N, Nanki M. Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Trans Med Imaging. 2004;23:330–9.CrossRefPubMed

11.

Li Z, Zhang X, Müller H, Zhang S. Large-scale retrieval for medical image analytics: a comprehensive review. Med Image Anal. 2018;43:66–84.CrossRefPubMed

12.

Taylor AJ, Cerqueira M, Hodgson JM, Mark D, Min J, O’Gara P, et al. ACCF/SCCT/ACR/AHA/ ASE/ASNC/NASCI/SCAI/SCMR 2010 appropriate use criteria for cardiac computed tomography: a report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the Society of Cardiovascular Computed Tomography, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the American Society of Nuclear Cardiology, the North American Society for Cardiovascular Imaging, the Society for Cardiovascular Angiography and Interventions, and the Society for Cardiovascular Magnetic Resonance. J Am Coll Cardiol. 2010;56:1864–94.CrossRefPubMed

13.

Budoff MJ, Dowe D, Jollis JG, Gitter M, Sutherland J, Halamert E, 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: results from the prospective multi-center ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol. 2008;52(21):1724–32. https://doi.org/10.1016/j.jacc.2008.07.031.CrossRefPubMed

14.

Yan RT, Miller JM, Rochitte CE, Dewey M, Niinuma H, Clouse ME, et al. Predictors of inaccurate coronary arterial stenosis assessment by CT angiography. JACC Cardiovasc Imaging. 2013;6:963–72.CrossRefPubMedPubMedCentral

15.

Fraser AG. A manifesto for cardiovascular imaging: addressing the human factor. EHJ Cardiovasc Imaging. 2017;18:1311–21.CrossRef

16.

Kahneman D. Thinking, fast and slow. New York: Farrar, Straus, and Giroux; 2011.

17.

Ovrehus KA, Munkholm H, Bøttcher M, Bøtker HE, Nørgaard BL. Coronary computed tomographic angiography in patients suspected of coronary artery disease: impact of observer experience on diagnostic performance and interobserver reproducibility. J Cardiovasc Comput Tomogr. 2010;4:186–9.CrossRefPubMed

18.

Sanchez-Martinez S, Duchateau N, Erdei T, Fraser AG, Bijnens BH, Piella G. Characterization of myocardial motion patterns by unsupervised multiple kernel learning. Med Image Anal. 2017;35:70–82.CrossRefPubMed

19.

•• Poplin R, Varadarajan A, Blumer K, Liu Y, McConnell M, Corrado G, et al. Predicting cardiovascular risk factors from retinal fundus photographs using deep learning. Nat Biomed Eng. 2018. https://doi.org/10.1038/s41551-018-0195-0. This important paper reports new indicators of heart disease risk present in the pre-existing biobank images of retinas through the application of deep learning.

20.

Dey D, Achenbach S, Schuhbaeck A, Pflederer T, Nakazato R, Slomka PJ, et al. Comparison of quantitative atherosclerotic plaque burden from coronary CT angiography in patients with first acute coronary syndrome and stable coronary artery disease. J Cardiovasc Comput Tomogr. 2014;8:a368–74.CrossRef

21.

Hell MM, Dey D, Marwan M, Achenbach S, Schmid J, Schuhbaeck A. Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography. Eur J Radiol. 2015;84:1502–8.CrossRefPubMed

22.

Kang D, Slomka PJ, Nakazato R, Arsanjani R, Cheng VY, Min JK, et al. Automated knowledge-based detection of nonobstructive and obstructive arterial lesions from coronary CT angiography. Med Phys. 2013;40:041912. https://doi.org/10.1118/1.4794480.CrossRefPubMed

23.

Gaur S, Ovrehus KA, Dey D, Leipsic J, Bøtker HE, Jensen JM, et al. Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions. Eur Heart J. 2016;7:1220–7.CrossRef

24.

Isgum I, Prokop M, Niemeijer M, Viergever MA, van Ginneken B. Automatic coronary calcium scoring in low-dose chest computed tomography. IEEE Trans Med Imaging. 2012;31:2322–34.CrossRefPubMed

25.

• Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Išgum I, et al. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal. 2016;34:123–36. The first study of its kind study that utilized the DL technique for the automatic identification of calcified plaques on cardiac computed tomography images.CrossRefPubMed

26.

Kang D, Dey D, Slomka PJ, Arsanjani R, Nakazato R, Ko H, et al. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from coronary CT angiography. J Med Imaging. 2015;2:014003.CrossRef

27.

Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D. Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging. 2008;27:1668–81. https://doi.org/10.1117/12.770710.CrossRefPubMed

28.

Ionasec RI, Voigt I, Georgescu B, Wang Y, Houle H, Vega-Higuera F, et al. Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE. IEEE Trans Med Imaging. 2010;29:1636–51. https://doi.org/10.1109/TMI.2010.2048756.CrossRefPubMed

29.

Liang L, Kong F, Martin C, Pham T, Wang Q, Duncan J, et al. Machine learning-based 3-D geometry reconstruction and modeling of aortic valve deformation using 3-D computed tomography images. Int J Numer Method Biomed Eng. 2017;33:e2827. https://doi.org/10.1002/cnm.2827.CrossRef

30.

Xiong G, Kola D, Heo R, Elmore K, Cho I, Min JK. Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest. Med Image Anal. 2015;24:77–89. https://doi.org/10.1016/j.media.2015.05.010.CrossRefPubMedPubMedCentral

31.

• Itu L, Rapaka S, Passerini T, Georgescu B, Schwemmer C, Schoebinger M, et al. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol. 2016;121:42–52. An important study, which showed the feasibility of a machine learning-based model for predicting fractional flow reserve.CrossRefPubMed

32.

•• Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, 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. 2016;38:500–7. A large study that demonstrated that all-cause mortality could be accurately predicted by the machine learning algorithms that combined image features from cardiac CT with clinical information.PubMedCentral

33.

• Henglin M, Stein G, Pavel V, Hushcha J. Machine learning approaches in cardiovascular imaging. Circ Cardiovasc Imaging. 2017;10:e005614. https://doi.org/10.1161/CIRCIMAGING.117.005614PH. A well-written review describing machine learning approaches including the strengths and limitations with regard to cardiovascular imaging.CrossRefPubMedPubMedCentral

34.

Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, et al. Deep learning in medical imaging: general overview. Korean J Radiol. 2017;18:570–84.CrossRefPubMedPubMedCentral

35.

https://github.com/openimages/dataset . Accessed on 18th Feb 2018.

36.

Fonseca CG, Backhaus M, Bluemke DA, Britten RD, Chung JD, Cowan BR, et al. The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics. 2011;27:2288–95.CrossRefPubMedPubMedCentral

37.

Khan S, Rahmani H, Shah SAA, Bennamoun M. A guide to convolutional neural networks for computer vision. San Rafael: Morgan & Claypool publishers; 2018.

Titel: Application of Artificial Intelligence in Coronary Computed Tomography Angiography
verfasst von: A. Selvarajah
M. Bennamoun
D. Playford
B. J. W Chow
Girish Dwivedi
Publikationsdatum: 01.06.2018
Verlag: Springer US
Erschienen in: Current Cardiovascular Imaging Reports / Ausgabe 6/2018
Print ISSN: 1941-9066
Elektronische ISSN: 1941-9074
DOI: https://doi.org/10.1007/s12410-018-9453-5

Neu im Fachgebiet Kardiologie

19.04.2024 | Vorhofflimmern | Nachrichten

Update Kardiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Application of Artificial Intelligence in Coronary Computed Tomography Angiography

Abstract

Purpose of Review

Recent Findings

Summary

Neu im Fachgebiet Kardiologie

Parodontalbehandlung verbessert Prognose bei Katheterablation

Antihypertensiva schützen auch alte Menschen noch vor Demenz

Sind Betablocker auch für ältere herzschwache Personen nützlich?

Stillende Frauen haben geringeres kardiovaskuläres Risiko

Update Kardiologie

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

Purpose of Review

Recent Findings

Summary

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Neu im Fachgebiet Kardiologie

Parodontalbehandlung verbessert Prognose bei Katheterablation

Antihypertensiva schützen auch alte Menschen noch vor Demenz

Sind Betablocker auch für ältere herzschwache Personen nützlich?

Stillende Frauen haben geringeres kardiovaskuläres Risiko

Update Kardiologie