Artificial intelligence applications in cardiology: a review
https://doi.org/10.15829/1560-4071-2024-5673
Abstract
The review article considers key applications of artificial intelligence (AI) in cardiology. The review includes subsections devoted to weak and strong AI used in clinical practice and cardiology health provision. The article describes the application options for AI in the analysis of electrocardiography, echocardiography, sonography, computed tomography, magnetic resonance imaging, and positron emission tomography of the heart data. The article briefly describes the aspects of using machine learning and artificial intelligence to process ambulance calls from patients with cardiac complaints, and considers AI applications in preventive cardiology. The review considers the potential of AI in the analysis of data arrays obtained during tonometry, pulse wave velocity measurement, and in biochemical studies. The paper also formulates the principles of strong AI (large language models) in cardiology health provision, identifies the main problems and difficulties in implementing the latest technology, and provides a conceptual scheme for implementing AI technology in a cardiology center. This paper highlights the key limitations of the large language model technology, such as the lack of standard algorithms for collecting and reviewing data, lack of understanding of the context, the inability of models to form expert conclusions, and the emergence of many problematic ethical characteristics when using large language models.
About the Authors
I. A. Soloviev I.A.
Pitirim Sorokin Syktyvkar State University
Russian Federation
Russian Federation
Syktyvkar
O. N. Kurochkina
Pitirim Sorokin Syktyvkar State University; Clinical Cardiology Dispensary
Russian Federation
Russian Federation
Syktyvkar
References
1. Fjelland R. Why General Artificial Intelligence will not be realized. Humanities and Social Sciences Communications. 2020;7(1). https://doi.org/10.1057/s41599-020-0494-4
2. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98. https://doi.org/10.7861/futurehosp.6-2-94.
3. Seetharam K, Balla S, Bianco C, Cheung J, Pachulski R, Asti D, Nalluri N, Tejpal A, Mir P, Shah J, Bhat P. Applications of machine learning in cardiology. Cardiology and Therapy. 2022;11(3):355-68.
4. Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol. 2019;16(8):601-607. https://doi.org/10.11909/j.issn.1671-5411.2019.08.002.
5. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18(7):465-478. https://doi.org/10.1038/s41569-020-00503-2.
6. Mincholé A, Camps J, Lyon A, Rodríguez B. Machine learning in the electrocardiogram. Journal of electrocardiology. 2019;57:S61-4.
7. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017;69(21):2657-2664. https://doi.org/10.1016/j.jacc.2017.03.571
8. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021 Jul;18(7):465-478. https://doi.org/10.1038/s41569-020-00503-2.
9. Tse G, Lee S, Zhou J, Liu T, Wong ICK, Mak C, Mok NS, Jeevaratnam K, Zhang Q, Cheng SH, Wong WT. Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses. Front Cardiovasc Med. 2021;8:608592. HTTPS://DOI.ORG//10.3389/fcvm.2021.608592.
10. Chung CT, Bazoukis G, Lee S, Liu Y, Liu T, Letsas KP, Armoundas AA, Tse G. Machine learning techniques for arrhythmic risk stratification: a review of the literature. Int J Arrhythmia. 2022;23:10. https://doi.org/10.1186/s42444-022-00062-2.
11. Lee S, Zhou J, Jeevaratnam K, Wong WT, Wong ICK, Mak C, Mok NS, Liu T, Zhang Q, Tse G. Paediatric/young versus adult patients with long QT syndrome. Open Heart. 2021;8(2):e001671. https://doi.org/10.1136/openhrt-2021-001671.
12. Kwon JM, Jo YY, Lee SY, Kim KH. Artificial intelligence using electrocardiography: strengths and pitfalls. Eur Heart J. 2021;42(30):2896-2898. https://doi.org/10.1093/eurheartj/ehab090
13. Swathy M, Saruladha K. A comparative study of classification and prediction of Cardio-Vascular Diseases (CVD) using Machine Learning and Deep Learning techniques. ICT Express. 2022;8(1):109-16. https://doi.org/10.1016/j.icte.2021.08.021
14. Salte IM, Østvik A, Smistad E, Melichova D, Nguyen TM, Karlsen S, Brunvand H, Haugaa KH, Edvardsen T, Lovstakken L, Grenne B. Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography. JACC Cardiovasc Imaging. 2021;14(10):1918-1928. https://doi.org/10.1016/j.jcmg.2021.04.018
15. Mamalakis M, Garg P, Nelson T, Lee J, Swift AJ, Wild JM, Clayton RH. Artificial Intelligence framework with traditional computer vision and deep learning approaches for optimal automatic segmentation of left ventricle with scar. Artificial Intelligence in Medicine. 2023:102610. https://doi.org/10.1016/j.artmed.2023.102610
16. Sun ZY, Li Q, Li J, Zhang MW, Zhu L, Geng J. Echocardiographic evaluation of the right atrial size and function: Relevance for clinical practice. American Heart Journal Plus: Cardiology Research and Practice. 2023:100274. https://doi.org/10.1016/j.ahjo.2023.100274
17. Sveric KM, Ulbrich S, Dindane Z, Winkler A, Botan R, Mierke J, Trausch A, Heidrich F, Linke A. Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging. Int J Cardiol. 2023. HTTPS://DOI.ORG//10.1016/j.ijcard.2023.131383
18. Wang S, Chauhan D, Patel H, Amir-Khalili A, da Silva IF, Sojoudi A, Friedrich S, Singh A, Landeras L, Miller T, Ameyaw K, Narang A, Kawaji K, Tang Q, Mor-Avi V, Patel AR. Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence. J Cardiovasc Magn Reson. 2022;24(1):27. HTTPS://DOI.ORG//10.1186/s12968-022-00861-5
19. Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, Rueckert D. Deep Learning for Cardiac Image Segmentation: A Review. Front Cardiovasc Med. 2020;7:25. HTTPS://DOI.ORG//10.3389/fcvm.2020.00025
20. Jani V, Danford DA, Thompson WR, Schuster A, Manlhiot C, Kutty S. The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine. Eur Heart J Digit Health. 2021;2(3):456-466. HTTPS://DOI.ORG//10.1093/ehjdh/ztab059
21. Kang S, Doroshow R, McConnaughey J, Shekhar R. Automated Identification of Innocent Still's Murmur in Children. IEEE Trans Biomed Eng. 2017;64(6):1326-1334. HTTPS://DOI.ORG//10.1109/TBME.2016.2603787
22. Grgic-Mustafic R, Baik-Schneditz N, Schwaberger B, Mileder L, Binder-Heschl C, Pansy J, Koestenberger M, Urlesberger B, Avian A, Pichler G. Novel algorithm to screen for heart murmurs using computer-aided auscultation in neonates: a prospective single center pilot observational study. Minerva Pediatr. 2019;71(3):221-228. HTTPS://DOI.ORG//10.23736/S0026-4946.18.04974-5
23. Lv J, Dong B, Lei H, Shi G, Wang H, Zhu F, Wen C, Zhang Q, Fu L, Gu X, Yuan J, Guan Y, Xia Y, Zhao L, Chen H. Artificial intelligence-assisted auscultation in detecting congenital heart disease. Eur Heart J Digit Health. 2021;2(1):119-124. HTTPS://DOI.ORG//10.1093/ehjdh/ztaa017
24. Swathy M, Saruladha K. A comparative study of classification and prediction of Cardio-Vascular Diseases (CVD) using Machine Learning and Deep Learning techniques. ICT Express. 2022;8(1):109-16.
25. Bustin A, Fuin N, Botnar RM, Prieto C. From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction. Front Cardiovasc Med. 2020;7:17. HTTPS://DOI.ORG//10.3389/fcvm.2020.00017. PMID:32158767 PMCID:PMC7051921
26. Pan J, Ng SM, Neubauer S, Rider OJ. Phenotyping heart failure by cardiac magnetic resonance imaging of cardiac macro- and microscopic structure: state of the art review. Eur Heart J Cardiovasc Imaging. 2023;24(10):1302-1317. HTTPS://DOI.ORG//10.1093/ehjci/jead124.
27. van Assen M, Muscogiuri G, Caruso D, Lee SJ, Laghi A, De Cecco CN. Artificial intelligence in cardiac radiology. Radiol Med. 2020;125(11):1186-1199. HTTPS://DOI.ORG//10.1007/s11547-020-01277-w
28. 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 Suppl 1:S58-S65. HTTPS://DOI.ORG//10.1097/RTI.0000000000000490
29. 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.
30. Cau R, Flanders A, Mannelli L, Politi C, Faa G, Suri JS, Saba L. Artificial intelligence in computed tomography plaque characterization: A review. Eur J Radiol. 2021;140:109767. HTTPS://DOI.ORG//10.1016/j.ejrad.2021.109767.
31. Muscogiuri G, Chiesa M, Baggiano A, Spadafora P, De Santis R, Guglielmo M, Scafuri S, Fusini L, Mushtaq S, Conte E, Annoni A, Formenti A, Mancini ME, Ricci F, Ariano FP, Spiritigliozzi L, Babbaro M, Mollace R, Maragna R, Giacari CM, Andreini D, Guaricci AI, Colombo GI, Rabbat MG, Pepi M, Sardanelli F, Pontone G. Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion. Eur J Nucl Med Mol Imaging. 2022;49(9):3119-3128. HTTPS://DOI.ORG//10.1007/s00259-022-05732-w.
32. Liu K, Werner T, Revheim M, Alavi A. Potential of Artificial Intelligence Cardiovascular PET imaging. J Nucl Med. 2022, 63 (supplement 2): 2753
33. Popescu C, Laudicella R, Baldari S, Alongi P, Burger I, Comelli A, Caobelli F. PET-based artificial intelligence applications in cardiac nuclear medicine. Swiss Med Wkly. 2022;152:w30123. HTTPS://DOI.ORG//10.4414/smw.2022.w30123.
34. Visco V, Izzo C, Mancusi C, Rispoli A, Tedeschi M, Virtuoso N, Giano A, Gioia R, Melfi A, Serio B, Rusciano MR, Di Pietro P, Bramanti A, Galasso G, D'Angelo G, Carrizzo A, Vecchione C, Ciccarelli M. Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve. J Cardiovasc Dev Dis. 2023;10(2):74. HTTPS://DOI.ORG//10.3390/jcdd10020074
35. Vargas JM, Bahloul MA, Laleg-Kirati TM. A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study. Front Physiol. 2023;14:1100570. https://doi.org//10.3389/fphys.2023.1100570
36. Blomberg SN, Folke F, Ersbøll AK, Christensen HC, Torp-Pedersen C, Sayre MR, Counts CR, Lippert FK. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation. 2019;138:322-329. https://doi.org//10.1016/j.resuscitation.2019.01.015
37. Attia ZI, Harmon DM, Dugan J, Manka L, Lopez-Jimenez F, Lerman A, Siontis KC, Noseworthy PA, Yao X, Klavetter EW, Halamka JD, Asirvatham SJ, Khan R, Carter RE, Leibovich BC, Friedman PA. Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction. Nat Med. 2022;28(12):2497-2503. https://doi.org//10.1038/s41591-022-02053-1
38. 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):1206-1212. https://doi.org//10.1016/S0140-6736(22)01637-3
39. Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. J Cardiovasc Med (Hagerstown). 2023;24(Suppl 2):e106-e115. https://doi.org//10.2459/JCM.0000000000001431
40. Ledziński Ł, Grześk G. Artificial Intelligence Technologies in Cardiology. J Cardiovasc Dev Dis. 2023;10(5):202. https://doi.org//10.3390/jcdd10050202
41. Sarraju A, Ouyang D, Itchhaporia D. The Opportunities and Challenges of Large Language Models in Cardiology. JACC: Advances. 2023;2(7):100438. https://doi.org/10.1016/j.jacadv.2023.100438
42. Sarraju A., Bruemmer D., Van Iterson E., Cho L., Rodriguez F., Laffin L. Appropriateness of cardiovascular disease prevention recommendations obtained from a popular online chat-based artificial intelligence model. JAMA. 2023;329:842-844. https://doi.org/10.1001/jama.2023.1044
43. Lee P., Bubeck S., Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023;388:1233-1239. https://doi.org/10.1056/NEJMsr2214184
44. Wu E., Wu K., Daneshjou R., Ouyang D., Ho D.E., Zou J. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021;27:582-584. https://doi.org/10.1038/s41591-021-01312-x
45. Skalidis I, Cagnina A, Fournier S. Use of large language models for evidence-based cardiovascular medicine. Eur Heart J Digit Health. 2023;4(5):368-369. https://doi.org/10.1093/ehjdh/ztad041
46. Klang E, Cohen-Shelly M, Lopez-Jimenez F. Leveraging Large Language Models to Enhance Digital Health in Cardiology: A Preview of a Cutting-Edge Language Generation Model. Mayo Clin Proc: Digital Health. 2023;1(2):105-8. https://doi.org/10.1016/j.mcpdig.2023.03.003
Supplementary files
- Artificial intelligence (AI) in cardiology is gradually becoming a generally accepted tool.
- Machine learning (weak AI) is already used in electro- and echocardiography, sonography, and diagnostic radiology of heart diseases, increasing their accuracy.
- Strong AI based on large language models is capable of revolutionizing continuous medical education by compiling digests for specialists.
- Large language models in cardiology, as they improve, are capable of accelerating filling out and analyzing medical documentation, which will lead to an increase in the speed and quality of health care.
- Despite the futuristic nature of large language models, there are many limitations and problems, such as ethical and professional ones, that prevent the implementation of this technology in practice.
Review
For citations:
Soloviev I.A. I.A., Kurochkina O.N. Artificial intelligence applications in cardiology: a review. Russian Journal of Cardiology. 2024;29(11S):5673. (In Russ.) https://doi.org/10.15829/1560-4071-2024-5673
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