Prospects for using machine learning to improve coronary angiography

Cover Page


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

Cardiovascular diseases pose the main threat to the population health of the Russian Federation and rank the first among the causes of death. Coronary heart disease has the highest standardized mortality rates among the population of the Russian Federation. Comprehensive diagnosis of coronary artery disease includes assessment of coronary atherosclerosis using both non-invasive methods, such as multispiral computed tomography of the coronary arteries, and invasive ones, including coronary angiography, and sometimes intravascular imaging. First two methods are the two most important diagnostic methods for coronary heart disease.

The widespread use of medical technologies based on artificial intelligence in recent years has led to the emergence of new diagnostic and therapeutic opportunities. Artificial intelligence has bridged the gap between massive datasets and useful information by processing and analyzing important data at an unprecedented rate.

The review identifies five potential cases with machine learning having significant prospects in the field of coronary angiography: improving quality and effectiveness, determining plaque characteristics, assessing hemodynamics, predicting disease outcomes and diagnosing non-atherosclerotic lesions of the coronary arteries. While machine learning has transformative potential in the field of coronary angiogram analysis, careful consideration of limitations, including data exchange protocols and interpretability of models is essential to fully exploit its potential and ensure optimal diagnosis and treatment of patients.

Full Text

Restricted Access

About the authors

Yurii A. Trusov

Samara State Medical University

Author for correspondence.
Email: secretplace@internet.ru
ORCID iD: 0000-0001-6407-3880
SPIN-code: 3203-5314

assistant

Russian Federation, Samara

Airina A. Vildanova

Bashkir State Medical University

Email: airinavildanowa@gmail.com
ORCID iD: 0009-0000-0625-9732
Russian Federation, Ufa

Amina N. Zagitova

Bashkir State Medical University

Email: zagitova.amina@mail.ru
ORCID iD: 0009-0006-9528-4019
Russian Federation, Ufa

Maria O. Simenenkova

V.I. Vernadsky Crimean Federal University

Email: masha.simenenkova@mail.ru
ORCID iD: 0009-0003-0523-3655
Russian Federation, Simferopol

Feride E. Settarova

V.I. Vernadsky Crimean Federal University

Email: ferideshka.settarova@gmail.com
ORCID iD: 0009-0003-5059-6105
Russian Federation, Simferopol

Zarina N. Rashitova

Pirogov Russian National Research Medical University

Email: rashitovazarina@yandex.ru
ORCID iD: 0009-0004-7890-5472
Russian Federation, Moscow

Anastasiia S. Kurchenko

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: kurchenko.anastasiia@yandex.ru
ORCID iD: 0009-0004-1055-3394
Russian Federation, Moscow

Yulia N. Lapshina

Penza State University

Email: jul1a110401@yandex.ru
ORCID iD: 0009-0001-0985-9212
SPIN-code: 2724-5472
Russian Federation, Penza

Anastasiia A. Romanova

Saint Petersburg State Pediatric Medical University

Email: romanna96@mail.ru
ORCID iD: 0009-0005-5675-835X
Russian Federation, Saint Petersburg

Konstantin M. Nechaev

Saint Petersburg State Pediatric Medical University

Email: kostanechaev16@gmail.com
ORCID iD: 0009-0005-6937-0215
Russian Federation, Saint Petersburg

Rodion A. Arkhipov

V.I. Vernadsky Crimean Federal University

Email: nomier@list.ru
ORCID iD: 0009-0004-3971-733X
Russian Federation, Simferopol

Akim R. Umerov

V.I. Vernadsky Crimean Federal University

Email: ufadime74@mail.ru
ORCID iD: 0009-0007-9134-1044
Russian Federation, Simferopol

Ildar I. Zainullin

Izhevsk State Medical Academy

Email: il116rus22@gmail.com
ORCID iD: 0009-0005-0812-6171
Russian Federation, Izhevsk

Kamila F. Bikmullina

Izhevsk State Medical Academy

Email: kbikmullina@mail.ru
ORCID iD: 0009-0003-2881-1876
Russian Federation, Izhevsk

References

  1. Kontsevaya AV, Mukaneeva DK, Ignatieva VI, et al. Economics of cardiovascular prevention in the Russian Federation. Russian Journal of Cardiology. 2023;28(9):19–26. EDN: KNLBZO doi: 10.15829/1560-4071-2023-5521
  2. Samorodskaya IV, Starinskaya MA, Boytsov SA. Changes of regional mortality rates from cardiovascular diseases and cognitive disorders in Russia over 2019-2021. Russian Journal of Cardiology. 2023;28(4):94–101. EDN: EYFUHW doi: 10.15829/1560-4071-2023-5256
  3. Chernyak AA, Deshko MS, Snezhitsky VA, et al. Percutaneous coronary interventions: intravascular imaging methods and measurement of intracoronary hemodynamics. Journal of the Grodno State Medical University. 2020;18(5):513–522. EDN: IQBKOL doi: 10.25298/2221-8785-2020-18-5-513-522
  4. Lichikaki VA, Mordovin VF, Falkovskaya AYu, et al. Features of coronary pathology and its relationship with markers of myocardial fibrosis in patients with resistant hypertension. Russian Journal of Cardiology. 2023;28(6):95–100. EDN: OPUXND doi: 10.15829/1560-4071-2023-5394
  5. Kovalskaya AN, Duplyakov DV. Biomarkers in assessing the vulnerability of atherosclerotic plaques: a narrative review. Rational Pharmacotherapy in Cardiology. 2023;19(3):282–288. EDN: DVSIQI doi: 10.20996/1819-6446-2023-2878
  6. Mironova OI, Isaev GO, Berdysheva MV, et al. Modern methods of assessment of physiological significance of coronary lesions: A review. Terapevticheskii arkhiv. 2023;95(4):341–346. EDN: ZLYNOW doi: 10.26442/00403660.2023.04.202169
  7. Darenskiy DI, Gramovich VV, Zharova EA, et al. Optimal cut-off points of instantaneous wave-free ratio in the assessment of the functional significance of coronary artery stenoses using noninvasive methods as reference. Eurasian Heart Journal. 2016;(4):34–41. EDN: XYKQLR
  8. Darenskiy DI, Gramovich VV, Zharova EA, et al. Comparison of diagnostic values of instantaneous wave-free ratio and fractional flow reserve with noninvasive methods for evaluating myocardial ischemia in assessment of the functional significance of intermediate coronary stenoses in patients with chronic ischemic heart disease. Kardiologiia. 2017;57(8):11–19. EDN: WQKQLB doi: 10.18087/cardio.2017.8.10012
  9. Semenova AA, Merkulova IN, Shariya MA, et al. Structural features of atherosclerotic plaques and their dynamics assessed by CT angiography in patients with acute coronary syndrome: a prospective study. Russian Cardiology Bulletin. 2021;16(4):66–75. EDN: SSCCNI doi: 10.17116/Cardiobulletin20211604166
  10. Williams MC, Moss AJ, Dweck M, et al. Coronary artery plaque charaeristics associated with adverse outcomes in the SCOT-HEART study. J Am Coll Cardiol. 2019;73(3):291–301. doi: 10.1016/j.jacc.2018.10.066
  11. Yeo KK. Artificial intelligence in cardiology: did it take off? Russian Journal for Personalized Medicine. 2022;2(6):16–22. EDN: UIENOT doi: 10.18705/2782-3806-2022-2-6-16-22
  12. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668–2679. doi: 10.1016/j.jacc.2018.03.521
  13. Sumin AN. Diagnostic algorithms in patients with chronic coronary syndromes – what does clinical practice show? Russian Journal of Cardiology. 2023;28(9):87–97. EDN: KAVIKE 5483. doi: 10.15829/1560-4071-2023-5483
  14. Barkalov MN, Atanesyan RV, Ageev FT, Matchin YuG. Clinical and economic efficiency of stenting with 40–60 mm stents in patients with extended coronary artery lesion. Russian Cardiology Bulletin. 2021;16(2):28-35. EDN: MHVGNO doi: 10.17116/Cardiobulletin20211602
  15. Jonas RA, Weerakoon S, Fisher R, et al. Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study. Clin Imaging. 2022;91:19–25. doi: 10.1016/j.clinimag.2022.08.005
  16. Zhang H, Mu L, Hu S, et al. Comparison of physician visual assessment with quantitative coronary angiography in assessment of stenosis severity in China. JAMA Intern Med. 2018;178(2):239–247. doi: 10.1001/jamainternmed.2017.7821
  17. 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: results from the prospective multicenter ACC URACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol. 2008;52(21):1724–1732. doi: 10.1016/j.jacc.2008.07.031
  18. Rugol LV, Son IM, Kirillov VI, Guseva SL. Organizational technologies that increase the availability of medical care for the population. Profilakticheskaya Meditsina. 2020;23(2):26–34. EDN: KBCBYP doi: 10.17116/profmed20202302126
  19. Hajiyev Ya, Shalbuzova K. Application of machine learning methods in cancer prediction and early detection. Sciences of Europe. 2022;108:46–50. EDN: PVXDIB doi: 10.5281/zenodo.7523833
  20. Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;392(10162):2388–2396. doi: 10.1016/s0140-6736(18)31645-3
  21. Lansberg MG, Christensen S, Kemp S, et al. Computed tomographic perfusion to predict response to recanalization in ischemic stroke. Ann Neurol. 2017;81(6):849–856. doi: 10.1002/ana.24953
  22. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi: 10.1038/nature21056
  23. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi: 10.1001/jama.2016.17216
  24. Al’Aref SJ, Anchouche K, Singh G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019;40(24):1975–1986. doi: 10.1093/ eurheartj/ehy404
  25. Lin A, Manral N, McElhinney P, et al. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health. 2022;4(4):e256–e265. doi: 10.1016/s2589-7500(22)00022-X
  26. 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):204–209. doi: 10.1016/j.jcct.2018.04.011
  27. Gusev AV, Gavrilov DV, Korsakov IN, et al. Prospects of using machine learning methods to predict cardiovascular diseases. Medical Doctor and IT. 2019;(3):41–47. EDN: KKDZXQ
  28. Liu F, Jang H, Kijowski R, et al. Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology. 2018;286(2):676–684. doi: 10.1148/radiol.2017170700
  29. Wang G, Li W, Ourselin S, Vercauteren T. Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Front Comput Neurosci. 2019;13:56. doi: 10.3389/fncom.2019.00056
  30. Azizov VA, Sultanova MJ, Uludag KI, Ephendieva LG. The possibilities of computed tomography in diagnose of the state of coronary arteries in patients with ischemic heart disease. Eurasian heart journal. 2014;(2):39–43. EDN: SMLQOD doi: 10.38109/2225-1685-2014-2-39-43
  31. Douglas PS, Hoffmann U, Patel MR, et al. Outcomes of anatomical versus functional testing for coronary artery disease. N Engl J Med. 2015;372(14):1291–1300. doi: 10.1056/NEJMoa1415516
  32. De la Garza-Salazar F, Lankenau-Vela DL, Cadena-Nuñez B, et al. The effect of functional and intra-coronary imaging techniques on fluoroscopy time, radiation dose and contrast volume during coronary angiography. Sci Rep. 2020;10(1):6950. doi: 10.1038/s41598-020-63791-1
  33. Koskinas KC, Nakamura M, Räber L, et al. Current use of intracoronary imaging in interventional practice — results of a European Association of Percutaneous Cardiovascular Interventions (EAPCI) and Japanese Association of Cardiovascular Interventions and Therapeutics (CVIT) Clinical Practice Survey. EuroIntervention. 2018;14(4):e475–e484. doi: 10.4244/eijy18m03_01
  34. 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(6):2655–2664. doi: 10.1007/s00330-017-5223-z
  35. Krittanawong C, Zhang H, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–2664. doi: 10.1016/j.jacc.2017.03.571
  36. Veselova TN, Ternovoy SK, Chepovskiy AM, et al. Evaluation of the fractional flow reserve by computer tomography data: comparison of the calculated parameters with the results of invasive measurements. Kardiologiia. 2021;61(7):28–35. EDN: WMFYUW doi: 10.18087/cardio.2021.7.n1540
  37. Darensky DI, Gramovich VV, Zharova EA, et al. The diagnostic value of measuring the momentary blood flow reserve versus non-invasive methods to detect myocardial ischemia in assessing the functional significance of borderline coronary artery stenoses. Terapevticheskii Arkhiv. 2017;89(4):15–21. EDN: YNEVZR doi: 10.17116/terarkh201789415-21
  38. Cho H, Lee JG, Kang SJ, et al. Angiography-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions. J Am Heart Assoc. 2019;8(4):e011685. doi: 10.1161/jaha.118.011685
  39. Kogame N, Ono M, Kawashima H, et al. The impact of coronary physiology on contemporary clinical decision making. JACC Cardiovasc Interv. 2020;13(14):1617–1638. doi: 10.1016/j.jcin.2020.04.040
  40. Zhuravlev KN, Vasilieva EYu, Sinitsyn VE, Spector AV. Calcium score as a screening method for cardiovascular disease diagnosis. Russian Journal of Cardiology. 2019;24(12):153–161. EDN: RKSFGF doi: 10.15829/1560-4071-2019-12-153-161
  41. Kral BG, Becker LC, Vaidya D, et al. Noncalcified coronary plaque volumes in healthy people with a family history of early onset coronary artery disease. Circ Cardiovasc Imaging. 2014;7(3):446–453. doi: 10.1161/circimaging.113.000980
  42. Nakanishi R, Slomka PJ, Rios R, et al. Machine learning adds to clinical and CAC assessments in predicting 10-year CHD and CVD deaths. JACC Cardiovasc Imaging. 2021;14(3):615–625. doi: 10.1016/j.jcmg.2020.08.024
  43. Zhukova NS, Shakhnovich RM, Merkulova IN, et al. Spontaneous coronary artery dissection. Kardiologiia. 2019;59(9):52–63. EDN: RIDCFO doi: 10.18087/cardio.2019.9.10269
  44. Khalikov AA, Kuznetsov KO, Iskuzhina LR, Khalikova LV. Forensic aspects of sudden autopsy-negative cardiac death. Forensic medical expertise. 2021;64(3):59-63. EDN: FOBSBA doi: 10.17116/sudmed20216403159
  45. Zainobidinov ShSh, Khelimsky DA, Baranov AA, et al. Modern aspects of diagnosis and treatment of patients with spontaneous coronary artery dissection. Cardiovascular Therapy and Prevention. 2022;21(8):106–118. EDN: WVUPET doi: 10.15829/1728-8800-2022-3193
  46. Taitakova BYu, Serdechnaya AYu, Sukmanova IА. Myocardial infarction in a patient after orthotopic heart transplantation, causes of development and management features. Ateroscleroz. 2022;18(1):81–86. EDN: WKTCBV doi: 10.52727/2078-256X-2022-18-1-81-86
  47. Voronina TS, Raskin VV, Frolova YuV, Dzemeshkevich SL. Coronary artery disease of the transplanted heart and systemic atherosclerosis similarities and differences. Atherosclerosis and dyslipidemia. 2014;(3):16–20. EDN: SISNQV
  48. Galin PYu, Gubanova TG. Microvascular angina pectoris as a problem of modern cardiology. Orenburgskij medicinskij vestnik. 2018;VI(1(21)):4–10. EDN: OJYCSA
  49. Marinescu MA, Löffler AI, Ouellette M, et al. Coronary microvascular dysfunction, microvascular angina, and treatment strategies. JACC Cardiovasc Imaging. 2015;8(2):210–220. doi: 10.1016/j.jcmg.2014.12.008
  50. Mathew RC, Bourque JM, Salerno M, Kramer CM. Cardiovascular imaging techniques to assess microvascular dysfunction. JACC Cardiovasc Imaging. 2020;13(7):1577–1590. doi: 10.1016/j.jcmg.2019.09.006
  51. Ford TJ, Stanley B, Sidik N, et al. 1-year outcomes of angina management guided by invasive coronary function testing (CorMicA). JACC Cardiovasc Interv. 2020;13(1):33–45. doi: 10.1016/j.jcin.2019.11.001
  52. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi: 10.1038/s41591-018-0300-7
  53. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–248. doi: 10.1146/annurev-bioeng-071516-044442
  54. Obermeyer Z, Emanuel EJ. Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–1219. doi: 10.1056/NEJMp1606181
  55. Brisimi TS, Chen R, Mela T, et al. Federated learning of predictive models from federated electronic health records. Int J Med Inform. 2018;112:59–67. doi: 10.1016/j.ijmedinf.2018.01.007
  56. Castelvecchi D. Can we open the black box of AI? Nature. 2016;538(7623):20–23. doi: 10.1038/538020a
  57. Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv. 2013. doi: 10.48550/arXiv.1312.6034
  58. Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, 27-30 June 2016. P. 2921–2929. doi: 10.1109/CVPR.2016.319
  59. Olah C, Mordvintsev A, Schubert L. Feature Visualization. Distill. Nov. 7, 2017. doi: 10.23915/distill.00007
  60. McGovern A, Lagerquist R, John Gagne D, et al. Making the black box more transparent: understanding the physical implications of machine learning. Bull Am Meteor Soc. 2019;100(11):2175–2199. doi: 10.1175/BAMS-D-18-0195.1
  61. Wagstaff KL, Lee J. Interpretable discovery in large image data sets. arXiv. 2018. doi: 10.48550/arXiv.1806.08340

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Figure. Study search algorithm

Download (385KB)
Indexing metadata

Copyright (c) 2024 Eco-Vector

License URL: https://eco-vector.com/for_authors.php#07
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 71733 от 08.12.2017.


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies