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01.11.2020 | Cardiac Computed Tomography (A. Crean and G. Small, Section Editors) | Ausgabe 11/2020

Artificial Intelligence in Cardiac CT: Automated Calcium Scoring and Plaque Analysis

Zeitschrift:: Current Cardiovascular Imaging Reports > Ausgabe 11/2020

Autoren:: Heon Lee, Tilman Emrich, U. Joseph Schoepf, Verena Brandt, Tyler J. Leonard, Hunter N. Gray, Vincent M. Giovagnoli, Danielle M. Dargis, Jeremy R. Burt, Christian Tesche

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Wichtige Hinweise

This article is part of the Topical Collection on Cardiac Computed Tomography

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Abstract

Purpose of Review

To review the current applications of artificial intelligence (AI) for coronary artery calcium scoring (CACS) and plaque analysis with their achievements and potential clinical impacts.

Recent Findings

Recent advances of AI-based technologies especially deep learning (DL) approaches in medical imaging have achieved substantial progress in automated detection and characterization of coronary atherosclerotic plaques, providing promising results for AI application in diagnosis and management of coronary artery disease.

Summary

To date, many studies investigated the potential role of DL in CACS and showed promising results for clinical application in a variety of CT examinations, demonstrating excellent agreement compared with manual scoring. DL-based approaches have also provided considerable progress in automated analysis of non-calcified plaque. Many investigations have shown that automated localization and classification of non-calcified plaque and luminal stenosis is feasible in cardiac CT angiography, although still challenging.

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Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1–25. https://doi.org/10.1016/j.jacc.2017.04.052. CrossRefPubMedPubMedCentral

Hecht HS. Coronary artery calcium scanning: past, present, and future. J Am Coll Cardiol Img. 2015;8(5):579–96. https://doi.org/10.1016/j.jcmg.2015.02.006. CrossRef

Pearson TA, Blair SN, Daniels SR, Eckel RH, Fair JM, Fortmann SP, et al. AHA guidelines for primary prevention of cardiovascular disease and stroke: 2002 update: consensus panel guide to comprehensive risk reduction for adult patients without coronary or other atherosclerotic vascular diseases. American Heart Association Science Advisory and Coordinating Committee. Circulation. 2002;106(3):388–91. https://doi.org/10.1161/01.cir.0000020190.45892.75. CrossRefPubMed

Chow BJ, Small G, Yam Y, Chen L, McPherson R, Achenbach S, et al. Prognostic and therapeutic implications of statin and aspirin therapy in individuals with nonobstructive coronary artery disease: results from the CONFIRM (COronary CT angiography evaluatioN For clinical outcomes: an InteRnational Multicenter registry) registry. Arterioscler Thromb Vasc Biol. 2015;35(4):981–9. https://doi.org/10.1161/atvbaha.114.304351. CrossRefPubMedPubMedCentral

Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15(4):827–32. https://doi.org/10.1016/0735-1097(90)90282-t. CrossRefPubMed

Divakaran S, Cheezum MK, Hulten EA, Bittencourt MS, Silverman MG, Nasir K, et al. Use of cardiac CT and calcium scoring for detecting coronary plaque: implications on prognosis and patient management. Br J Radiol. 2015;88(1046):20140594. https://doi.org/10.1259/bjr.20140594. CrossRefPubMedPubMedCentral

Blaha MJ, Mortensen MB, Kianoush S, Tota-Maharaj R, Cainzos-Achirica M. Coronary artery calcium scoring: is it time for a change in methodology? J Am Coll Cardiol Img. 2017;10(8):923–37. https://doi.org/10.1016/j.jcmg.2017.05.007. CrossRef

Insull W Jr. The pathology of atherosclerosis: plaque development and plaque responses to medical treatment. Am J Med. 2009;122(1 Suppl):S3–s14. https://doi.org/10.1016/j.amjmed.2008.10.013. CrossRefPubMed

Hampe N, Wolterink JM, van Velzen SGM, Leiner T, Isgum I. Machine learning for assessment of coronary artery disease in cardiac CT: a survey. Front Cardiovasc Med. 2019;6:172. https://doi.org/10.3389/fcvm.2019.00172. CrossRefPubMedPubMedCentral

10.

Rodríguez-Granillo GA, Reynoso E, Capuñay C, Antoniades C, Shaw LJ, Carrascosa P. Prognostic value of vascular calcifications and regional fat depots derived from conventional chest computed tomography. J Thorac Imaging. 2019;34(1):33–40. https://doi.org/10.1097/rti.0000000000000370. CrossRefPubMed

11.

Erbel R, Mohlenkamp S, Moebus S, Schmermund A, Lehmann N, Stang A, et al. Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study. J Am Coll Cardiol. 2010;56(17):1397–406. https://doi.org/10.1016/j.jacc.2010.06.030. CrossRefPubMed

12.

Virmani R, Burke AP, Farb A, Kolodgie FD. Pathology of the vulnerable plaque. J Am Coll Cardiol. 2006;47(8 Suppl):C13–8. https://doi.org/10.1016/j.jacc.2005.10.065. CrossRefPubMedPubMedCentral

13.

Saremi F, Achenbach S. Coronary plaque characterization using CT. AJR Am J Roentgenol. 2015;204(3):W249–60. https://doi.org/10.2214/ajr.14.13760. CrossRefPubMed

14.

Cury RC, Abbara S, Achenbach S, Agatston A, Berman DS, Budoff MJ, et al. CAD-RADS: coronary artery disease - reporting and data system: an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Am Coll Radiol. 2016;13(12 Pt A):1458–66.e9. https://doi.org/10.1016/j.jacr.2016.04.024. CrossRefPubMed

15.

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 multicenter 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

16.

Schoepf UJ, Zwerner PL, Savino G, Herzog C, Kerl JM, Costello P. Coronary CT angiography. Radiology. 2007;244(1):48–63. https://doi.org/10.1148/radiol.2441052145. CrossRefPubMed

17.

Pugliese F, Hunink MG, Gruszczynska K, Alberghina F, Malago R, van Pelt N, et al. Learning curve for coronary CT angiography: what constitutes sufficient training? Radiology. 2009;251(2):359–68. https://doi.org/10.1148/radiol.2512080384. CrossRefPubMed

18.

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;34:123–36. https://doi.org/10.1016/j.media.2016.04.004. CrossRefPubMed

19.

Martin SS, van Assen M, Rapaka S, Hudson HT Jr, Fischer AM, Varga-Szemes A, et al. Evaluation of a deep learning-based automated CT coronary artery calcium scoring algorithm. J Am Coll Cardiol Img. 2020;13(2 Pt 1):524–6. https://doi.org/10.1016/j.jcmg.2019.09.015. CrossRef

20.

Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I. A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging. 2019;38(7):1588–98. https://doi.org/10.1109/tmi.2018.2883807. CrossRefPubMed

21.

Lee H, Martin S, Burt JR, Bagherzadeh PS, Rapaka S, Gray HN, et al. Machine learning and coronary artery calcium scoring. Curr Cardiol Rep. 2020;22(9):90. https://doi.org/10.1007/s11886-020-01337-7. CrossRefPubMed

22.

• Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318–28. https://doi.org/10.1148/radiol.2018171820 This article reviews current applications of ML techniques in medical imaging and discusses their future impact in clinical practice. CrossRefPubMedPubMedCentral

23.

Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35. https://doi.org/10.1186/s41747-018-0061-6. CrossRefPubMedPubMedCentral

24.

Sharma P, Suehling M, Flohr T, Comaniciu D. Artificial intelligence in diagnostic imaging: status quo, challenges, and future opportunities. J Thorac Imaging. 2020;35:S11–6. https://doi.org/10.1097/rti.0000000000000499. CrossRefPubMed

25.

Fischer AM, Yacoub B, Savage RH, Martinez JD, Wichmann JL, Sahbaee P, et al. Machine learning/deep neuronal network: routine application in chest computed tomography and workflow considerations. J Thorac Imaging. 2020;35(Suppl 1):S21–s7. https://doi.org/10.1097/rti.0000000000000498. CrossRefPubMed

26.

van Assen M, Banerjee I, De Cecco CN. Beyond the artificial intelligence hype: what lies behind the algorithms and what we can achieve. J Thorac Imaging. 2020;35(Suppl 1):S3–s10. https://doi.org/10.1097/rti.0000000000000485. CrossRefPubMed

27.

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 Hear J. 2019;27(9):403–13. https://doi.org/10.1007/s12471-019-01311-1. CrossRef

28.

Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol. 2019;73(11):1317–35. https://doi.org/10.1016/j.jacc.2018.12.054. CrossRefPubMedPubMedCentral

29.

Monti CB, Codari M, van Assen M, De Cecco CN, Vliegenthart R. Machine learning and deep neural networks applications in computed tomography for coronary artery disease and myocardial perfusion. J Thorac Imaging. 2020;35:S58–65. https://doi.org/10.1097/rti.0000000000000490. CrossRefPubMed

30.

Sandstedt M, Henriksson L, Janzon M, Nyberg G, Engvall J, De Geer J, et al. Evaluation of an AI-based, automatic coronary artery calcium scoring software. Eur Radiol. 2020;30(3):1671–8. https://doi.org/10.1007/s00330-019-06489-x. CrossRefPubMed

31.

von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ, Albrecht MH, van Assen M, De Santis D, et al. Impact of coronary computerized tomography angiography-derived plaque quantification and machine-learning computerized tomography fractional flow reserve on adverse cardiac outcome. Am J Cardiol. 2019;124(9):1340–8. https://doi.org/10.1016/j.amjcard.2019.07.061. CrossRef

32.

Benton SM Jr, Tesche C, De Cecco CN, Duguay TM, Schoepf UJ, Bayer RR 2nd. Noninvasive derivation of fractional flow reserve from coronary computed tomographic angiography: a review. J Thorac Imaging. 2018;33(2):88–96. https://doi.org/10.1097/rti.0000000000000289. CrossRefPubMed

33.

Schwartz FR, Koweek LM, Nørgaard BL. Current evidence in cardiothoracic imaging: computed tomography-derived fractional flow reserve in stable chest pain. J Thorac Imaging. 2019;34(1):12–7. https://doi.org/10.1097/rti.0000000000000369. CrossRefPubMed

34.

Retson TA, Besser AH, Sall S, Golden D, Hsiao A. Machine learning and deep neural networks in thoracic and cardiovascular imaging. J Thorac Imaging. 2019;34(3):192–201. https://doi.org/10.1097/rti.0000000000000385. CrossRefPubMed

35.

Tesche C, Bauer MJ, Baquet M, Hedels B, Straube F, Hartl S, et al. Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning. Eur Radiol. 2020. https://doi.org/10.1007/s00330-020-07083-2.

36.

Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959;3:210–29. CrossRef

37.

Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–60. https://doi.org/10.1126/science.aaa8415. CrossRefPubMed

38.

Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol. 2017;208(4):754–60. https://doi.org/10.2214/ajr.16.17224. CrossRefPubMed

39.

Gonzalez G, Washko GR, Estepar RS. Automated agatston score computation in a large dataset of non ecg-gated chest computed tomography. Proc IEEE Int Symp Biomed Imaging. 2016;2016:53–7. https://doi.org/10.1109/isbi.2016.7493209. CrossRefPubMedPubMedCentral

40.

Xie Y, Liu S, Miller A, Miller J, Markowitz S, Akhund A, et al. Coronary artery calcification identification and labeling in low-dose chest CT images. SPIE Medical Imaging. SPIE; 2017.

41.

Lee SM, Seo JB, Yun J, Cho YH, Vogel-Claussen J, Schiebler ML, et al. Deep learning applications in chest radiography and computed tomography: current state of the art. J Thorac Imaging. 2019;34(2):75–85. https://doi.org/10.1097/rti.0000000000000387. CrossRefPubMed

42.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539. CrossRefPubMedPubMedCentral

43.

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(4):570–84. https://doi.org/10.3348/kjr.2017.18.4.570. CrossRefPubMedPubMedCentral

44.

Rawat W, Wang Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 2017;29(9):2352–449. https://doi.org/10.1162/NECO_a_00990. CrossRefPubMedPubMedCentral

45.

Schlett CL, Nattenmuller J, Tsuchiya N, Vogel-Claussen J, Kauczor HU, Levin D, et al. Noncontrast chest computed tomographic imaging of obesity and the metabolic syndrome: part I cardiovascular findings. J Thorac Imaging. 2019;34(2):116–25. https://doi.org/10.1097/rti.0000000000000391. CrossRefPubMed

46.

Torres FS, Venkatesh V, Nguyen ET, Jimenez-Juan L, Crean AM. Coronary calcium scan acquisition before coronary CT angiography: limited benefit or useful addition? AJR Am J Roentgenol. 2013;200(1):66–73. https://doi.org/10.2214/ajr.12.8643. CrossRefPubMed

47.

De Filippo M, Capasso R. Coronary computed tomography angiography (CCTA) and cardiac magnetic resonance (CMR) imaging in the assessment of patients presenting with chest pain suspected for acute coronary syndrome. Ann Translat Med. 2016;4(13):255. https://doi.org/10.21037/atm.2016.06.30. CrossRef

48.

Pursnani A, Chou ET, Zakroysky P, Deano RC, Mamuya WS, Woodard PK, et al. Use of coronary artery calcium scanning beyond coronary computed tomographic angiography in the emergency department evaluation for acute chest pain: the ROMICAT II trial. Circ Cardiovasc Imaging. 2015;8(3). doi: https://doi.org/10.1161/circimaging.114.002225.

49.

Chiles C, Duan F, Gladish GW, Ravenel JG, Baginski SG, Snyder BS, et al. Association of coronary artery calcification and mortality in the National Lung Screening Trial: a comparison of three scoring methods. Radiology. 2015;276(1):82–90. https://doi.org/10.1148/radiol.15142062. CrossRefPubMedPubMedCentral

50.

Pavitt CW, Harron K, Lindsay AC, Ray R, Zielke S, Gordon D, et al. Deriving coronary artery calcium scores from CT coronary angiography: a proposed algorithm for evaluating stable chest pain. Int J Card Imaging. 2014;30(6):1135–43. https://doi.org/10.1007/s10554-014-0439-3. CrossRef

51.

Otton JM, Lonborg JT, Boshell D, Feneley M, Hayen A, Sammel N, et al. A method for coronary artery calcium scoring using contrast-enhanced computed tomography. J Cardiovasc Comput Tomogr. 2012;6(1):37–44. https://doi.org/10.1016/j.jcct.2011.11.004. CrossRefPubMed

52.

Fischer AM, Eid M, De Cecco CN, Gulsun MA, van Assen M, Nance JW, et al. Accuracy of an artificial intelligence deep learning algorithm implementing a recurrent neural network with long short-term memory for the automated detection of calcified plaques from coronary computed tomography angiography. J Thorac Imaging. 2020;35(Suppl 1):S49–s57. https://doi.org/10.1097/rti.0000000000000491. CrossRefPubMed

53.

Hecht HS, de Siqueira ME, Cham M, Yip R, Narula J, Henschke C, et al. Low- vs. standard-dose coronary artery calcium scanning. Eur Heart J Cardiovasc Imaging. 2015;16(4):358–63. https://doi.org/10.1093/ehjci/jeu218. CrossRefPubMed

54.

Nakazato R, Dey D, Gutstein A, Le Meunier L, Cheng VY, Pimentel R, et al. Coronary artery calcium scoring using a reduced tube voltage and radiation dose protocol with dual-source computed tomography. J Cardiovasc Comput Tomogr. 2009;3(6):394–400. https://doi.org/10.1016/j.jcct.2009.10.002. CrossRefPubMed

55.

Caruso D, De Santis D, Biondi T, Panvini N, Zerunian M, Rivosecchi F, et al. Half-dose coronary artery calcium scoring: impact of iterative reconstruction. J Thorac Imaging. 2019;34(1):18–25. https://doi.org/10.1097/rti.0000000000000340. CrossRefPubMed

56.

de Vos BD, Wolterink JM, Leiner T, de Jong PA, Lessmann N, Isgum I. Direct automatic coronary calcium scoring in cardiac and chest CT. IEEE Trans Med Imaging. 2019;38(9):2127–38. https://doi.org/10.1109/tmi.2019.2899534. CrossRefPubMed

57.

Kurkure U, Chittajallu DR, Brunner G, Le YH, Kakadiaris IA. A supervised classification-based method for coronary calcium detection in non-contrast CT. Int J Card Imaging. 2010;26(7):817–28. https://doi.org/10.1007/s10554-010-9607-2. CrossRef

58.

Brunner G, Chittajallu DR, Kurkure U, Kakadiaris IA. Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data. Int J Card Imaging. 2010;26(7):829–38. https://doi.org/10.1007/s10554-010-9608-1. CrossRef

59.

Sanchez CI, Niemeijer M, Isgum I, Dumitrescu A, Suttorp-Schulten MS, Abramoff MD, et al. Contextual computer-aided detection: improving bright lesion detection in retinal images and coronary calcification identification in CT scans. Med Image Anal. 2012;16(1):50–62. https://doi.org/10.1016/j.media.2011.05.004. CrossRefPubMed

60.

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(12):2322–34. https://doi.org/10.1109/tmi.2012.2216889. CrossRefPubMed

61.

Shahzad R, van Walsum T, Schaap M, Rossi A, Klein S, Weustink AC, et al. Vessel specific coronary artery calcium scoring: an automatic system. Acad Radiol. 2013;20(1):1–9. https://doi.org/10.1016/j.acra.2012.07.018. CrossRefPubMed

62.

Wesarg S, Khan MF, Firle EA. Localizing calcifications in cardiac CT data sets using a new vessel segmentation approach. J Digit Imaging. 2006;19(3):249–57. https://doi.org/10.1007/s10278-006-9947-6. CrossRefPubMedPubMedCentral

63.

Eilot D, Goldenberg R. Fully automatic model-based calcium segmentation and scoring in coronary CT angiography. Int J Comput Assist Radiol Surg. 2014;9(4):595–608. https://doi.org/10.1007/s11548-013-0955-y. CrossRefPubMed

64.

Ahmed W, de Graaf MA, Broersen A, Kitslaar PH, Oost E, Dijkstra J, et al. Automatic detection and quantification of the Agatston coronary artery calcium score on contrast computed tomography angiography. Int J Card Imaging. 2015;31(1):151–61. https://doi.org/10.1007/s10554-014-0519-4. CrossRef

65.

Lessmann N, Išgum I, Setio AA, de Vos B, Ciompi F, de Jong P, et al. Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT. SPIE Medical Imaging. SPIE; 2016.

66.

Lessmann N, van Ginneken B, Zreik M, de Jong PA, de Vos BD, Viergever MA, et al. Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions. IEEE Trans Med Imaging. 2018;37(2):615–25. https://doi.org/10.1109/tmi.2017.2769839. CrossRefPubMed

67.

•• van Velzen SGM, Lessmann N, Velthuis BK, Bank IEM, van den Bongard D, Leiner T, et al. Deep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocols. Radiology. 2020:191621. Doi: https://doi.org/10.1148/radiol.2020191621. This study validated the extensibility of DL in CACS and demonstrated promising results suggesting that a particular DL model trained for a specific CT type adapts well to diverse types of CT examinations.

68.

Tesche C, Plank F, De Cecco CN, Duguay TM, Albrecht MH, Varga-Szemes A, et al. Prognostic implications of coronary CT angiography-derived quantitative markers for the prediction of major adverse cardiac events. J Cardiovasc Comput Tomogr. 2016;10(6):458–65. https://doi.org/10.1016/j.jcct.2016.08.003. CrossRefPubMed

69.

Dey D, Cheng VY, Slomka PJ, Nakazato R, Ramesh A, Gurudevan S, et al. Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography. J Cardiovasc Comput Tomogr. 2009;3(6):372–82. https://doi.org/10.1016/j.jcct.2009.09.004. CrossRefPubMed

70.

Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A, 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):e007217. https://doi.org/10.1161/CIRCIMAGING.117.007217. CrossRefPubMed

71.

Tesche C, De Cecco CN, Baumann S, Renker M, McLaurin TW, Duguay TM, et al. Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology. 2018;288(1):64–72. https://doi.org/10.1148/radiol.2018171291. CrossRefPubMed

72.

Tesche C, Gray HN. Machine learning and deep neural networks applications in coronary flow assessment: the case of computed tomography fractional flow reserve. J Thorac Imaging. 2020;35(Suppl 1):S66–s71. https://doi.org/10.1097/rti.0000000000000483. CrossRefPubMed

73.

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:ehw188. https://doi.org/10.1093/eurheartj/ehw188.

74.

van Rosendael AR, Maliakal G, Kolli KK, Beecy A, Al’Aref SJ, Dwivedi A, 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. https://doi.org/10.1016/j.jcct.2018.04.011. CrossRefPubMed

75.

•• Johnson KM, Johnson HE, Zhao Y, Dowe DA, Staib LH. Scoring of coronary artery disease characteristics on coronary CT angiograms by using machine learning. Radiology. 2019;292(2):354–62. https://doi.org/10.1148/radiol.2019182061 This study demonstrated that ML methods better discriminate patients with MACE and may provide a more individualized approach to patient care compared with the current CTA scoring system. CrossRefPubMed

76.

van Assen M, Varga-Szemes A, Schoepf UJ, Duguay TM, Hudson HT, Egorova S, et al. Automated plaque analysis for the prognostication of major adverse cardiac events. Eur J Radiol. 2019;116:76–83. https://doi.org/10.1016/j.ejrad.2019.04.013. CrossRefPubMed

77.

Dey D, Schepis T, Marwan M, Slomka PJ, Berman DS, Achenbach S. Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US. Radiology. 2010;257(2):516–22. https://doi.org/10.1148/radiol.10100681. CrossRefPubMed

78.

Dey D, Gaur S, Ovrehus KA, Slomka PJ, Betancur J, Goeller M, et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol. 2018;28(6):2655–64. https://doi.org/10.1007/s00330-017-5223-z. CrossRefPubMedPubMedCentral

79.

Hou KY, Tsujioka K, Yang CC. Optimization of HU threshold for coronary artery calcium scans reconstructed at 0.5-mm slice thickness using iterative reconstruction. J Appl Clin Med Phys. 2020;21(2):111–20. https://doi.org/10.1002/acm2.12806. CrossRefPubMed

Titel: Artificial Intelligence in Cardiac CT: Automated Calcium Scoring and Plaque Analysis
Autoren:: Heon Lee
Tilman Emrich
U. Joseph Schoepf
Verena Brandt
Tyler J. Leonard
Hunter N. Gray
Vincent M. Giovagnoli
Danielle M. Dargis
Jeremy R. Burt
Christian Tesche
Publikationsdatum: 01.11.2020
DOI: https://doi.org/10.1007/s12410-020-09549-9
Verlag: Springer US
Zeitschrift: Current Cardiovascular Imaging Reports
Ausgabe 11/2020
Print ISSN: 1941-9066
Elektronische ISSN: 1941-9074

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