Роль искусственного интеллекта в кардиологии

Искусственный интеллект (ИИ) обладает огромным потенциалом для повышения качества оказания медицинской помощи, улучшения методов диагностики и лечения. ИИ позволяет перевести научные исследования на принципиально новый уровень. В статье рассматриваются наиболее важные направления применения ИИ в кардиологии. Использование ИИ для ускорения принятия клинических решений, дистанционного наблюдения за пациентами, анализа томографических изображений, фенотипирования пациентов, в том числе с помощью метаболомного анализа, определения риска развития осложнений и многих других направлений.

1. Morley J, Murphy L, Mishra A, Joshi I, Karpathakis K. Governing Data and Artificial Intelligence for Health Care: Developing an International Understanding. JMIR Formative Research. 2022;6(1):e31623. DOI: 10.2196/31623

2. Vandenbroucke F, Michel M, Ackerman N, Briganti G. L’adoption de l’Intelligence Artificielle dans les hôpitaux en Belgique. Barometer. 2022. 26p. Av. at: https://www.msdconnect.be/fr/wp-content/uploads/sites/15/2022/05/FR_BarometreIA_Belgique.pdf.

3. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine. 2020;3(1):17. DOI: 10.1038/s41746-020-0221-y

4. Almyranti M, Sutherland E, Ash N, Eiszele S. Artificial Intelligence and the health workforce: Perspectives from medical associations on AI in health. OECD artificial intelligence papers. 2024;28:1–54. DOI: 10.1787/9a31d8af-en

5. Hu J-R, Power JR, Zannad F, Lam CSP. Artificial intelligence and digital tools for design and execution of cardiovascular clinical trials. European Heart Journal. 2024;ehae794. [Epub ahead of print]. DOI: 10.1093/eurheartj/ehae794

6. Harmon SA, Tuncer S, Sanford T, Choyke PL, Turkbey B. Artificial intelligence at the intersection of pathology and radiology in prostate cancer. Diagnostic and Interventional Radiology. 2019;25(3):183–8. DOI: 10.5152/dir.2019.19125

7. Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB et al. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Internal Medicine. 2023;183(6):589–96. DOI: 10.1001/jamainternmed.2023.1838

8. Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172–80. DOI: 10.1038/s41586-023-06291-2

9. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nature Reviews Cardiology. 2021;18(7):465–78. DOI: 10.1038/s41569-020-00503-2

10. Christopoulos G, Graff-Radford J, Lopez CL, Yao X, Attia ZI, Rabinstein AA et al. Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study. Circulation: Arrhythmia and Electrophysiology. 2020;13(12):e009355. DOI: 10.1161/CIRCEP.120.009355

11. 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. Nature Medicine. 2019;25(1):70–4. DOI: 10.1038/s41591-018-0240-2

12. Kwon J, Kim K-H, Jeon K-H, Kim HM, Kim MJ, Lim S-M et al. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification. Korean Circulation Journal. 2019;49(7):629–39. DOI: 10.4070/kcj.2018.0446

13. Cho J, Lee B, Kwon J-M, Lee Y, Park H, Oh B-H et al. Artificial Intelligence Algorithm for Screening Heart Failure with Reduced Ejection Fraction Using Electrocardiography. ASAIO Journal. 2021;67(3):314–21. DOI: 10.1097/MAT.0000000000001218

14. Кузнецова Н.О., Сагирова Ж.Н., Губина А.Ю., Захаров И.П., Фашафша З.З.А., Сыркина Е.А. и др. Новый алгоритм оценки диастолической дисфункции левого желудочка по данным одноканальной электрокардиограммы с использованием машинного обучения. Кардиология и сердечно-сосудистая хирургия. 2022;15(5):496-500. DOI: 10.17116/kardio202215051496

15. 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. The Lancet. 2019;394(10201):861–7. DOI: 10.1016/S0140-6736(19)31721-0

16. Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP et al. Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiology. 2019;4(5):428–36. DOI: 10.1001/jamacardio.2019.0640

17. Sanches CA, Silva GA, Librantz AFH, Sampaio LMM, Belan PA. Wearable Devices to Diagnose and Monitor the Progression of COVID-19 Through Heart Rate Variability Measurement: Systematic Review and Meta-Analysis. Journal of Medical Internet Research. 2023;25:e47112. DOI: 10.2196/47112

18. Lubitz SA, Faranesh AZ, Selvaggi C, Atlas SJ, McManus DD, Singer DE et al. Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study. Circulation. 2022;146(19):1415– 24. DOI: 10.1161/CIRCULATIONAHA.122.060291

19. Kwon J, Jo Y-Y, Lee SY, Kang S, Lim S-Y, Lee MS et al. Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG. Diagnostics. 2022;12(3):654. DOI: 10.3390/diagnostics12030654

20. Qian J, McDonough DJ, Gao Z. The Effectiveness of Virtual Reality Exercise on Individual’s Physiological, Psychological and Rehabilitative Outcomes: A Systematic Review. International Journal of Environmental Research and Public Health. 2020;17(11):4133. DOI: 10.3390/ijerph17114133

21. Seng KP, Ang L-M, Peter E, Mmonyi A. Machine Learning and AI Technologies for Smart Wearables. Electronics. 2023;12(7):1509. DOI: 10.3390/electronics12071509

22. Wrzeciono A, Cieślik B, Kiper P, Szczepańska-Gieracha J, Gajda R. Exploratory analysis of the effectiveness of virtual reality in cardiovascular rehabilitation. Scientific Reports. 2024;14(1):281. DOI: 10.1038/ s41598-023-50788-9

23. Wu W, Dasgupta S, Ramirez EE, Peterson C, Norman GJ. Classification Accuracies of Physical Activities Using Smartphone Motion Sensors. Journal of Medical Internet Research. 2012;14(5):e130. DOI: 10.2196/jmir.2208

24. Bae S, Borac S, Emre Y, Wang J, Wu J, Kashyap M et al. Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms. Communications Medicine. 2022;2(1):40. DOI: 10.1038/s43856-022-00102-x

25. Amrulloh YA, Abeyratne UR, Swarnkar V, Herath D, Triasih R, Setyati A. HMM Based Cough Sound Analysis for Classifying Pneumonia and Asthma in Pediatric Population. V. 65. P. 852-855. [DOI: 10.1007/978-981-10-5122-7_213]. In: EMBEC & NBC 2017. - Singapore: Springer Singapore; 2018. [ISBN: 978-981-10-5121-0]

26. Morawski K, Ghazinouri R, Krumme A, Lauffenburger JC, Lu Z, Durfee E et al. Association of a Smartphone Application With Medication Adherence and Blood Pressure Control: The MedISAFE-BP Randomized Clinical Trial. JAMA Internal Medicine. 2018;178(6):802–9. DOI: 10.1001/jamainternmed.2018.0447

27. Petito LC, Anthony L, Peprah Y, Lee JY, Li J, Sato H et al. Blood pressure outcomes at 12 months in primary care patients prescribed remote physiological monitoring for hypertension: a prospective cohort study. Journal of Human Hypertension. 2023;37(12):1091–7. DOI: 10.1038/s41371-023-00850-w

28. Alshahrani NS, Hartley A, Howard J, Hajhosseiny R, Khawaja S, Seligman H et al. Randomized Trial of Remote Assessment of Patients After an Acute Coronary Syndrome. Journal of the American College of Cardiology. 2024;83(23):2250–9. DOI: 10.1016/j.jacc.2024.03.398

29. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC. European Journal of Heart Failure. 2022;24(1):4–131. DOI: 10.1002/ejhf.2333

30. Boehmer JP, Cremer S, Abo-Auda WS, Stokes DR, Hadi A, McCann PJ et al. Impact of a Novel Wearable Sensor on Heart Failure Rehospitalization: An Open-Label Concurrent-Control Clinical Trial. JACC: Heart Failure. 2024;12(12):2011–22. DOI: 10.1016/j.jchf.2024.07.022

31. Lindenfeld J, Zile MR, Desai AS, Bhatt K, Ducharme A, Horstmanshof D et al. Haemodynamic-guided management of heart failure (GUIDE-HF): a randomised controlled trial. The Lancet. 2021;398(10304):991–1001. DOI: 10.1016/S0140-6736(21)01754-2

32. Girerd N, Barbet V, Seronde M, Benchimol H, Jagu A, Tartière J et al. Association of a remote monitoring programme with all‐cause mortality and hospitalizations in patients with heart failure: National‐ scale, real‐world evidence from a 3‐year propensity score analysis of the TELESAT‐HF study. European Journal of Heart Failure. 2025;ejhf.3563. [Epub ahead of print]. DOI: 10.1002/ejhf.3563

33. Emel’yanov A.V., Zheleznykh E.A., Kozhevnikova M.V., Zektser V.Yu., Kamalova A.É., Privalova E.V. et al. Influence of remote digital observations on quality of life, compliance, and clinical outcomes in patients with chronic heart failure. Biomedical Engineering. 2024;57(5):311– 5. DOI: 10.1007/s10527-023-10322-7

34. Jimenez C, Saavedra E, Del Campo G, Santamaria A. Alexa-Based Voice Assistant for Smart Home Applications. IEEE Potentials. 2021;40(4):31–8. DOI: 10.1109/MPOT.2020.3002526

35. Sara JDS, Maor E, Borlaug B, Lewis BR, Orbelo D, Lerman LO et al. Non-invasive vocal biomarker is associated with pulmonary hypertension. PLOS ONE. 2020;15(4):e0231441. DOI: 10.1371/journal.pone.0231441

36. Murton OM, Dec GW, Hillman RE, Majmudar MD, Steiner J, Guttag JV et al. Acoustic Voice and Speech Biomarkers of Treatment Status during Hospitalization for Acute Decompensated Heart Failure. Applied Sciences. 2023;13(3):1827. DOI: 10.3390/app13031827

37. Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M et al. Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction. Circulation. 2015;131(3):269–79. DOI: 10.1161/CIRCULATIONAHA.114.010637

38. Kobayashi M, Huttin O, Magnusson M, Ferreira JP, Bozec E, Huby A-C et al. Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals. JACC: Cardiovascular Imaging. 2022;15(2):193–208. DOI: 10.1016/j.jcmg.2021.07.004

39. Monzo L, Bresso E, Dickstein K, Pitt B, Cleland JGF, Anker SD et al. Machine learning approach to identify phenotypes in patients with ischaemic heart failure with reduced ejection fraction. European Journal of Heart Failure. 2024;ejhf.3547. [Epub ahead of print]. DOI: 10.1002/ejhf.3547

40. Kozhevnikova MV, Belenkov YN, Shestakova KM, Ageev AA, Markin PA, Kakotkina AV et al. Metabolomic profile for understanding heart failure classifications. BioRxiv. 2024. [Preprint. DOI: 10.1101/2024.11.13.623517]

41. Madan N, Lucas J, Akhter N, Collier P, Cheng F, Guha A et al. Artificial intelligence and imaging: Opportunities in cardio-oncology. American Heart Journal Plus: Cardiology Research and Practice. 2022;15:100126. DOI: 10.1016/j.ahjo.2022.100126

42. Martinez DS-L, Noseworthy PA, Akbilgic O, Herrmann J, Ruddy KJ, Hamid A et al. Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography. American Heart Journal Plus: Cardiology Research and Practice. 2022;15:100129. DOI: 10.1016/j.ahjo.2022.100129

43. Chen H, Ouyang D, Baykaner T, Jamal F, Cheng P, Rhee J-W. Artificial intelligence applications in cardio-oncology: Leveraging high dimensional cardiovascular data. Frontiers in Cardiovascular Medicine. 2022;9:941148. DOI: 10.3389/fcvm.2022.941148

44. Chang W-T, Liu C-F, Feng Y-H, Liao C-T, Wang J-J, Chen Z-C et al. An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline. Archives of Toxicology. 2022;96(10):2731–7. DOI: 10.1007/s00204-022-03341-y

45. Zheng Y, Chen Z, Huang S, Zhang N, Wang Y, Hong S et al. Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline. Reviews in Cardiovascular Medicine. 2023;24(10):296. DOI: 10.31083/j.rcm2410296

46. Wu S, Cao W, Fu S, Yao B, Yang Z, Yin C et al. CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity. ArXiv. 2024. [Preprint. DOI: 10.48550/ARXIV.2410.04592]

47. Goecks J, Jalili V, Heiser LM, Gray JW. How Machine Learning Will Transform Biomedicine. Cell. 2020;181(1):92–101. DOI: 10.1016/j.cell.2020.03.022

48. Varoquaux G, Cheplygina V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digital Medicine. 2022;5(1):48. DOI: 10.1038/s41746-022-00592-y

49. Barberis E, Khoso S, Sica A, Falasca M, Gennari A, Dondero F et al. Precision Medicine Approaches with Metabolomics and Artificial Intelligence. International Journal of Molecular Sciences. 2022;23(19):11269. DOI: 10.3390/ijms231911269

50. Sharma A, Lysenko A, Jia S, Boroevich KA, Tsunoda T. Advances in AI and machine learning for predictive medicine. Journal of Human Genetics. 2024;69(10):487–97. DOI: 10.1038/s10038-024-01231-y

51. Krittanawong C, Johnson KW, Choi E, Kaplin S, Venner E, Murugan M et al. Artificial Intelligence and Cardiovascular Genetics. Life. 2022;12(2):279. DOI: 10.3390/life12020279

52. Ni G, Zeng J, Revez JA, Wang Y, Zheng Z, Ge T et al. A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biological Psychiatry. 2021;90(9):611–20. DOI: 10.1016/j.biopsych.2021.04.018

53. Aung N, Vargas JD, Yang C, Cabrera CP, Warren HR, Fung K et al. Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development. Circulation. 2019;140(16):1318–30. DOI: 10.1161/CIRCULATIONAHA.119.041161

54. Khurshid S, Lazarte J, Pirruccello JP, Weng L-C, Choi SH, Hall AW et al. Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass. Nature Communications. 2023;14(1):1558. DOI: 10.1038/s41467-023-37173-w

55. Kwon O-S, Hong M, Kim T-H, Hwang I, Shim J, Choi E-K et al. Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence. Open Heart. 2022;9(1):e001898. DOI: 10.1136/openhrt-2021-001898

56. McGranaghan P, Saxena A, Rubens M, Radenkovic J, Bach D, Schleußner L et al. Predictive value of metabolomic biomarkers for cardiovascular disease risk: a systematic review and meta-analysis. Biomarkers. 2020;25(2):101–11. DOI: 10.1080/1354750X.2020.1716073

57. Migdadi L, Lambert J, Telfah A, Hergenröder R, Wöhler C. Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR). Computational and Structural Biotechnology Journal. 2021;19:5047–58. DOI: 10.1016/j.csbj.2021.08.048

58. Jung S, Ahn E, Koh SB, Lee S-H, Hwang G-S. Purine metabolitebased machine learning models for risk prediction, prognosis, and diagnosis of coronary artery disease. Biomedicine & Pharmacotherapy. 2021;139:111621. DOI: 10.1016/j.biopha.2021.111621

59. Osipov A, Nikolic O, Gertych A, Parker S, Hendifar A, Singh P et al. The Molecular Twin artificial-intelligence platform integrates multiomic data to predict outcomes for pancreatic adenocarcinoma patients. Nature Cancer. 2024;5(2):299–314. DOI: 10.1038/s43018-023-00697-7

60. Antoniades C, Patel P, Antonopoulos AS. Using artificial intelligence to study atherosclerosis, predict risk and guide treatments in clinical practice. European Heart Journal. 2023;44(6):437–9. DOI: 10.1093/eurheartj/ehac751

61. Antonopoulos AS, Sanna F, Sabharwal N, Thomas S, Oikonomou EK, Herdman L et al. Detecting human coronary inflammation by imaging perivascular fat. Science Translational Medicine. 2017;9(398):eaal2658. DOI: 10.1126/scitranslmed.aal2658

62. Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal. 2019;40(43):3529–43. DOI: 10.1093/eurheartj/ehz592

63. Gadodia G, Evans M, Weunski C, Ho A, Cargill A, Martin C. Evaluation of an augmented reality navigational guidance platform for percutaneous procedures in a cadaver model. Journal of Medical Imaging. 2024;11(6):062602. DOI: 10.1117/1.JMI.11.6.062602

64. Cai YY, Chui CK, Ye XZ, Fan Z, Anderson JH. Tactile VR for hand–eye coordination in simulated PTCA. Computers in Biology and Medicine. 2006;36(2):167–80. DOI: 10.1016/j.compbiomed.2004.10.002

65. Unlu O, Shin J, Mailly CJ, Oates MF, Tucci MR, Varugheese M et al. Retrieval Augmented Generation Enabled Generative Pre-Trained Transformer 4 (GPT-4) Performance for Clinical Trial Screening. MedRxiv. 2024. [Preprint. DOI: 10.1101/2024.02.08.24302376]