ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ ПРИ СЕРДЕЧНО-СОСУДИСТЫХ ЗАБОЛЕВАНИЯХ: ПЕРСПЕКТИВЫ ДИАГНОСТИКИ И ТЕРАПИИ
Аннотация
Искусственный интеллект, метод извлечения информации из сложной базы данных с использованием сложных компьютерных алгоритмов, нашел свое применение в медицинской сфере. Методы искусственного интеллекта продемонстрировали потенциал ускорения прогресса диагностики и лечения сердечно-сосудистых заболеваний, включая сердечную недостаточность, фибрилляцию предсердий, пороки клапанов сердца, гипертрофическую кардиомиопатию, врожденные пороки сердца и так далее. В клиническом сценарии было доказано, что искусственный интеллект хорошо применяется для диагностики сердечно-сосудистых заболеваний, повышения эффективности вспомогательных инструментов, стратификации и типирования заболеваний, а также прогнозирования результатов. Цель этого обзора — представить текущие применения искусственного интеллекта при сердечно-сосудистых заболеваниях, которые могут позволить врачам лучше понять специфику и применение искусственного интеллекта в клинической практике.
Литература
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8. Ko WY, Siontis KC, Attia ZI, Carter RE, Kapa S, Ommen SR, et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. J Am Coll Cardiol. 2020;75(7):722–33.
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16. Vaid A, Johnson KW, Badgeley MA, Somani SS, Bicak M, Landi I, et al. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging. 2022;15(3):395–410.
17. Saikrishnan N, Kumar G, Sawaya FJ, Lerakis S, Yoganathan AP. Accurate assessment of aortic stenosis: a review of diagnostic modalities and hemodynamics. Circulation. 2014;129(2):244–53.
18. Elias P, Poterucha TJ, Rajaram V, Moller LM, Rodriguez V, Bhave S, et al. Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease. J Am Coll Cardiol. 2022;80(6):613–26.
19. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18(7):465–78.
20. Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG, et al. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med. 2010;363(17):1597–607.
21. Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, et al. Deep learning-based algorithm for detecting aortic stenosis using electrocardiography. J Am Heart Assoc. 2020;9(7):e014717.
22. Cohen-Shelly M, Attia ZI, Friedman PA, Ito S, Essayagh BA, Ko WY, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J. 2021;42(30):2885–96.
23. Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, Davis EM, et al. Screening for atrial fibrillation: US preventive services task force recommendation statement. JAMA. 2022;327(4):360–7.
24. Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, et al. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022;400(10359):1206–12.
25. Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, 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.
26. Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J. 2020;41(46):4400–11.
27. Yan BP, Lai WHS, Chan CKY, Au ACK, Freedman B, Poh YC, et al. High-throughput, contact-free detection of atrial fibrillation from video with deep learning. JAMA Cardiol. 2020;5(1):105–7.
28. de Couto G, Ouzounian M, Liu PP. Early detection of myocardial dysfunction and heart failure. Nat Rev Cardiol. 2010;7(6):334–44.
29. Japp AG, Gulati A, Cook SA, Cowie MR, Prasad SK. The diagnosis and evaluation of dilated cardiomyopathy. J Am Coll Cardiol. 2016;67(25):2996–3010.
30. Shrivastava S, Cohen-Shelly M, Attia ZI, Rosenbaum AN, Wang L, Giudicessi JR, et al. Artificial intelligence-enabled electrocardiography to screen patients with dilated cardiomyopathy. Am J Cardiol. 2021;155:121–7.
31. Maron BJ, Haas TS, Murphy CJ, Ahluwalia A, Rutten-Ramos S. Incidence and causes of sudden death in U.S. college athletes. J Am Coll Cardiol. 2014;63(16):1636–43.
32. Donofrio MT, Moon-Grady AJ, Hornberger LK, Copel JA, Sklansky MS, Abuhamad A, et al. Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American heart association. Circulation. 2014;129(21):2183–242.
33. Sun HY, Proudfoot JA, McCandless RT. Prenatal detection of critical cardiac outflow tract anomalies remains suboptimal despite revised obstetrical imaging guidelines. Congenit Heart Dis. 2018;13(5):748–56.
34. Arnaout R, Curran L, Zhao Y, Levine JC, Chinn E, Moon-Grady AJ. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med. 2021;27(5):882–91.
35. Karwath A, Bunting KV, Gill SK, Tica O, Pendleton S, Aziz F, et al. Redefining beta-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. Lancet. 2021;398(10309):1427–35.
36. Proietti M, Vitolo M, Harrison SL, Lane DA, Fauchier L, Marin F, et al. Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational research programme in AF (EORP-AF) general long-term registry. BMC Med. 2021;19(1):256.
37. Howard JP, Cook CM, van de Hoef TP, Meuwissen M, de Waard GA, van Lavieren MA, et al. Artificial intelligence for aortic pressure waveform analysis during coronary angiography: machine learning for patient safety. JACC Cardiovasc Interv. 2019;12(20):2093–101.
38. Yang DY, Nie ZQ, Liao LZ, Zhang SZ, Zhou HM, Sun XT, et al. Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: an exploratory study of the SPRINT trial. Eur J Prev Cardiol. 2019;26(16):1693–706.
39. Raghunath S, Ulloa Cerna AE, Jing L, vanMaanen DP, Stough J, Hartzel DN, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med. 2020;26(6):886–91.
40. Toya T, Ahmad A, Attia Z, Cohen-Shelly M, Ozcan I, Noseworthy PA, et al. Vascular aging detected by peripheral endothelial dysfunction is associated with ECG-derived physiological aging. J Am Heart Assoc. 2021;10(3):e018656.
41. Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun. 2021;12(1):715.
42. Min HS, Ryu D, Kang SJ, Lee JG, Yoo JH, Cho H, et al. Prediction of coronary stent underexpansion by pre-procedural intravascular ultrasound-based deep learning. JACC Cardiovasc Interv. 2021;14(9):1021–9.
2. Montull L, Slapsinskaite-Dackeviciene A, Kiely J, Hristovski R, Balague N. Integrative proposals of sports monitoring: subjective outperforms objective monitoring. Sports Med Open. 2022;8(1):41.
3. Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25(1):70–4.
4. Attia ZI, Cape S, Noseworthy PA, Lopez-Jimenez F, Friedman PA. Artificial intelligence ECG to detect left ventricular dysfunction in COVID-19: a case series. Mayo Clin Proc. 2020;95(11):2464–6.
5. Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021;27(5):815–9.
6. Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394(10201):861–7.
7. Kwon JM, Cho Y, Jeon KH, Cho S, Kim KH, Baek SD, et al. A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study. Lancet Digit Health. 2020;2(7):e358–67.
8. Ko WY, Siontis KC, Attia ZI, Carter RE, Kapa S, Ommen SR, et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. J Am Coll Cardiol. 2020;75(7):722–33.
9. Kwon JM, Kim KH, Medina-Inojosa J, Jeon KH, Park J, Oh BH. Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography. J Heart Lung Transplant. 2020;39(8):805–14.
10. Cho H, Keenan G, Madandola OO, Dos Santos FC, Macieira TGR, Bjarnadottir RI, et al. Assessing the usability of a clinical decision support system: a heuristic evaluation. JMIR Hum Factors. 2022;9(2):e31758.
11. Emile SH, Hamid HKS. Fighting COVID-19, a place for artificial intelligence. Transbound Emerg Dis. 2020;67(5):1754–5.
12. Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming diabetes care through artificial intelligence: the future is here. Popul Health Manag. 2019;22(3):229–42.
13. Yan Y, Zhang JW, Zang GY, Pu J. The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine? J Geriatr Cardiol. 2019;16(8):585–91.
14. Goodswen SJ, Barratt JLN, Kennedy PJ, Kaufer A, Calarco L, Ellis JT. Machine learning and applications in microbiology. FEMS Microbiol Rev. 2021;45:5.
15. Zhu R, Jiang C, Wang X, Wang S, Zheng H, Tang H. Privacy-preserving construction of generalized linear mixed model for biomedical computation. Bioinformatics. 2020;36(1):128–35.
16. Vaid A, Johnson KW, Badgeley MA, Somani SS, Bicak M, Landi I, et al. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging. 2022;15(3):395–410.
17. Saikrishnan N, Kumar G, Sawaya FJ, Lerakis S, Yoganathan AP. Accurate assessment of aortic stenosis: a review of diagnostic modalities and hemodynamics. Circulation. 2014;129(2):244–53.
18. Elias P, Poterucha TJ, Rajaram V, Moller LM, Rodriguez V, Bhave S, et al. Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease. J Am Coll Cardiol. 2022;80(6):613–26.
19. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18(7):465–78.
20. Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG, et al. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med. 2010;363(17):1597–607.
21. Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, et al. Deep learning-based algorithm for detecting aortic stenosis using electrocardiography. J Am Heart Assoc. 2020;9(7):e014717.
22. Cohen-Shelly M, Attia ZI, Friedman PA, Ito S, Essayagh BA, Ko WY, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J. 2021;42(30):2885–96.
23. Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, Davis EM, et al. Screening for atrial fibrillation: US preventive services task force recommendation statement. JAMA. 2022;327(4):360–7.
24. Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, et al. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022;400(10359):1206–12.
25. Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, 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.
26. Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J. 2020;41(46):4400–11.
27. Yan BP, Lai WHS, Chan CKY, Au ACK, Freedman B, Poh YC, et al. High-throughput, contact-free detection of atrial fibrillation from video with deep learning. JAMA Cardiol. 2020;5(1):105–7.
28. de Couto G, Ouzounian M, Liu PP. Early detection of myocardial dysfunction and heart failure. Nat Rev Cardiol. 2010;7(6):334–44.
29. Japp AG, Gulati A, Cook SA, Cowie MR, Prasad SK. The diagnosis and evaluation of dilated cardiomyopathy. J Am Coll Cardiol. 2016;67(25):2996–3010.
30. Shrivastava S, Cohen-Shelly M, Attia ZI, Rosenbaum AN, Wang L, Giudicessi JR, et al. Artificial intelligence-enabled electrocardiography to screen patients with dilated cardiomyopathy. Am J Cardiol. 2021;155:121–7.
31. Maron BJ, Haas TS, Murphy CJ, Ahluwalia A, Rutten-Ramos S. Incidence and causes of sudden death in U.S. college athletes. J Am Coll Cardiol. 2014;63(16):1636–43.
32. Donofrio MT, Moon-Grady AJ, Hornberger LK, Copel JA, Sklansky MS, Abuhamad A, et al. Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American heart association. Circulation. 2014;129(21):2183–242.
33. Sun HY, Proudfoot JA, McCandless RT. Prenatal detection of critical cardiac outflow tract anomalies remains suboptimal despite revised obstetrical imaging guidelines. Congenit Heart Dis. 2018;13(5):748–56.
34. Arnaout R, Curran L, Zhao Y, Levine JC, Chinn E, Moon-Grady AJ. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med. 2021;27(5):882–91.
35. Karwath A, Bunting KV, Gill SK, Tica O, Pendleton S, Aziz F, et al. Redefining beta-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. Lancet. 2021;398(10309):1427–35.
36. Proietti M, Vitolo M, Harrison SL, Lane DA, Fauchier L, Marin F, et al. Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational research programme in AF (EORP-AF) general long-term registry. BMC Med. 2021;19(1):256.
37. Howard JP, Cook CM, van de Hoef TP, Meuwissen M, de Waard GA, van Lavieren MA, et al. Artificial intelligence for aortic pressure waveform analysis during coronary angiography: machine learning for patient safety. JACC Cardiovasc Interv. 2019;12(20):2093–101.
38. Yang DY, Nie ZQ, Liao LZ, Zhang SZ, Zhou HM, Sun XT, et al. Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: an exploratory study of the SPRINT trial. Eur J Prev Cardiol. 2019;26(16):1693–706.
39. Raghunath S, Ulloa Cerna AE, Jing L, vanMaanen DP, Stough J, Hartzel DN, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med. 2020;26(6):886–91.
40. Toya T, Ahmad A, Attia Z, Cohen-Shelly M, Ozcan I, Noseworthy PA, et al. Vascular aging detected by peripheral endothelial dysfunction is associated with ECG-derived physiological aging. J Am Heart Assoc. 2021;10(3):e018656.
41. Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun. 2021;12(1):715.
42. Min HS, Ryu D, Kang SJ, Lee JG, Yoo JH, Cho H, et al. Prediction of coronary stent underexpansion by pre-procedural intravascular ultrasound-based deep learning. JACC Cardiovasc Interv. 2021;14(9):1021–9.
Опубликована
2024-03-01
Как цитировать
ИГНАТЕНКО, Г. А. et al.
ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ ПРИ СЕРДЕЧНО-СОСУДИСТЫХ ЗАБОЛЕВАНИЯХ: ПЕРСПЕКТИВЫ ДИАГНОСТИКИ И ТЕРАПИИ.
Новообразование, [S.l.], v. 15, n. 4, p. 10-16, мар. 2024.
ISSN 2521-117X.
Доступно на: <http://donetsk-onco.com/neoplasm/index.php/Neoplasm/article/view/505>. Дата доступа: 27 апр. 2024
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