Authors : Shaik Parveen; Boddu Ajay; Kudukuntla Venkat Krishna

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/4urky4c2

Scribd : https://tinyurl.com/3m9bkajr

DOI : https://doi.org/10.38124/ijisrt/25jul1721

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : Imagine a heart that not only beats but also learns, adapts, and predicts its own destiny—welcome to the age of the “Digital Heart”. In this era-defining review, unveil how Artificial Intelligence (AI) is rewriting the rules of cardiovascular care: lightning-fast algorithms now pore over thousands of ECG tracings in the blink of an eye to unmask hidden arrhythmias; deep neural networks scrutinize cardiac CT scans to spot microscopic plaque vulnerabilities before they strike; and telemonitoring platforms armed with predictive analytics forewarn of heart failure flare-ups days in advance. From AI- driven decision engines that tailor therapies to your unique genetic and lifestyle fingerprint, to self-supervising models that learn from millions of anonymized patient journeys while safeguarding privacy, these innovations promise to transform every heartbeat into a data point for personalized health. Yet, as we stand on this precipice of possibility, questions abound: Can we truly trust black-box predictions? Will federated learning bridge disparities or deepen them? How soon before algorithms replace our stethoscopes? Join us on this electrifying journey through AI’s frontier in cardiovascular medicine— where each discovery sparks the next leap toward a future in which machines not only mend hearts but foresee and forestall disease in real time.

Keywords : Digital Heart, Artificial Intelligence, Arrhythmias, Cardiovascular Medicine, Lifestyle Fingerprint.

References :

1. World Health Organization. Cardiovascular diseases (CVDs) [Internet]. Geneva: World Health Organization; 2023
2. World Health Organization. Cardiovascular diseases (CVDs) [Internet]. Geneva: World Health Organization; 2023
3. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668–79. doi:10.1016/j.jacc.2018.03.521.
4. Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Truong QA, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol. 2019;73(11):1317–35. doi:10.1016/j.jacc.2018.12.054.
5. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43. doi:10.1136/svn-2017-000101.
6. Seetharam K, Shrestha S, Sengupta PP. Artificial intelligence in cardiovascular medicine. Curr Treat Options Cardiovasc Med.
7. Kumar R, Pal M, Tiwari P, Elhoseny M. A comprehensive review of machine learning for heart disease prediction. Front Artif Intell. 2025;5:1583459.
8. Almansouri NE, Ghaban W, Abdur-Rahman FO, Al-Mashraqi F, Abuhmed T, Mohammed MA. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence and Machine Learning. Front Cardiovasc Med. 2024;11:11002715.
9. Wan S, Rauf HT, Lali MIU, Habib A, Hong W. Gradient boosting-based prediction approach for cardiovascular disease diagnosis. Comput Biol Med. 2024;169:107634.
10. Abdullah M, Khan HF, Zameer A. Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron. Front Artif Intell. 2024;7:1539588.
11. El-Sofany H, et al. A proposed technique for predicting heart disease using machine learning methods. Sci Rep. 2024;14:74656.
12. Joshi MK, Dembla D, Bhatia S. Utilizing machine learning algorithms for cardiovascular disease prediction. Med Eng Phys. 2025;140:104347.
13. Seetharam K., Kagiyama N., and Sengupta P. P., Application of mobile health, telemedicine and artificial intelligence to echocardiography, Echo Research and Practice. (2019) 6, no. 2, R41–R52, https://doi.org/10.1530/erp-18-0081, 2-s2.0-85065758860.
14. Ravi D., Wong C., Deligianni F. et al., Deep learning for health informatics, IEEE Journal of Biomedical and Health Informatics. (2017) 21, no. 1, 4–21, https://doi.org/10.1109/jbhi.2016.2636665, 2-s2.0-85014952213.
15. Krittanawong C., Zhang H., Wang Z., Aydar M., and Kitai T., Artificial intelligence in precision cardiovascular medicine, Journal of the American College of Cardiology. (2017) 69, no. 21, 2657–2664, https://doi.org/10.1016/j.jacc.2017.03.571, 2-s2.0-85019606440.
16. Knackstedt, C.; Bekkers, S.C.; Schummers, G.; Schreckenberg, M. Fully Automated Versus Stand of Left Ventricular Ejection Fraction and Longitudinal Strain: The FAST-EFs Multicenter Study. J. Am. Coll. Cardiol. 2015, 66, 1456–1466. [CrossRef]
17. Narula, S.; Shameer, K.; Salem Omar, A.M.; Dudley, J.T.; Sengupta, P.P. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J. Am. Coll. Cardiol. 2016, 68, 2287–2295. [CrossRef] [PubMed
18. Zhang, J.; Gajjala, S.; Agrawal, P.; Tison, G.H.; Hallock, L.A.; Beussink-Nelson, L.; Lassen, M.H.; Fan, E.; Aras, M.A.; Jordan, C.; et al. Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation 2018, 138, 1623–1635. [CrossRef] [PubMed]
19. Moghaddasi, H.; Nourian, S. Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos. Comput. Biol. Med. 2016, 73, 47–55. [CrossRef] [PubMed]
20. Playford, D.; Bordin, E.; Mohamad, R.; Stewart, S.; Strange, G. Enhanced Diagnosis of Severe Aortic Stenosis Using Artificial Intelligence: A Proof-of-Concept Study of 530,871 Echocardiograms. JACC Cardiovasc. Imaging 2020, 13, 1087–1090. [CrossRef]
21. Playford, D.; Bordin, E.; Mohamad, R.; Stewart, S.; Strange, G. Enhanced Diagnosis of Severe Aortic Stenosis Using Artificial Intelligence: A Proof-of-Concept Study of 530,871 Echocardiograms. JACC Cardiovasc. Imaging 2020, 13, 1087–1090. [CrossRef]
22. Playford, D.; Bordin, E.; Mohamad, R.; Stewart, S.; Strange, G. Enhanced Diagnosis of Severe Aortic Stenosis Using Artificial Intelligence: A Proof-of-Concept Study of 530,871 Echocardiograms. JACC Cardiovasc. Imaging 2020, 13, 1087–1090. [CrossRef]
23. Oever LB van den, Vonder M, Assen M van, Ooijen PMA van, de Bock GH, Xie XQ, et al. Application of artificial intelligence in cardiac CT: From basics to clinical practice. Clin Imaging. 2020;69:1–15.
24. Oshi M, Melo DP, Ouyang D, Slomka PJ, Williams MC, Dey D. Current and Future Applications of Artificial Intelligence in Cardiac CT. Curr Cardiol Rep. 2023 Mar;25(3):109-117.
25. Dundas J, et al. Artificial Intelligence–based Coronary Stenosis Quantification at Coronary CT Angiography. Radiol Cardiothorac Imaging. 2023 Nov;5(6):e230124. doi:10.1148/ryct.230124.
26. CERTAIN Trial Investigators. Abstract 13467: Artificial Intelligence Guided Coronary CT Angiography Impacts Care Beyond Conventional Assessment: The CERTAIN Trial. Circulation. 2023 Nov;148(Suppl_1):13467.
27. Slomka PJ, Betancur J, Liang JX, et al. Advances in SPECT and PET hardware. Prog Cardiovasc Dis. 2015;57(6):566–578.
28. Xu Y, Gong K, Liu C, et al. Denoising of PET images using deep learning: a review of techniques and clinical applications. Med Image Anal. 2021;70:102002.
29. Oktay O, Ferrante E, Kamnitsas K, et al. Super-resolution of cardiac MRI using generative adversarial networks. IEEE Trans Med Imaging. 2018;37(12):2730–2743.
30. Dey D, Gaur S, Ovrehus KA, et al. Integrated prediction of lesion-specific ischemia from quantitative coronary CT angiography using machine learning. JACC Cardiovasc Imaging. 2018;11(9):1372–1381.
31. Baessler B, Mannil M, Oebel S, et al. Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images. Radiology. 2018;286(1):103–112.
32. Chen C, Bai W, Davies RH, et al. Automated functional assessment of the left ventricle from cardiac MRI using supervised learning. Med Image Anal. 2019;50:44–54.
33. Nakajima, K.; Kudo, T.; Nakata, T.; Kiso, K.; Kasai, T.; Taniguchi, Y.; Matsuo, S.; Momose, M.; Nakagawa, M.; Sarai, M.; et al. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: A Japanese multicenter study. Eur. J. Nucl. Med. Mol. Imaging 2017, 44, 2280–2289. [CrossRef]
34. Sanchez-Martinez, S.; Duchateau, N.; Erdei, T.; Kunszt, G.; Aakhus, S.; Degiovanni, A.; Marino, P.; Carluccio, E.; Piella, G.; Fraser, A.G.; et al. Machine Learning Analysis of Left Ventricular Function to Characterize Heart Failure with Preserved Ejection Fraction. Circ. Cardiovasc. Imaging 2018, 11, e007138. [CrossRef]
35. Inan, O.T.; Baran Pouyan, M.; Javaid, A.Q.; Dowling, S.; Etemadi, M.; Dorier, A.; Heller, J.A.; Bicen, A.O.; Roy, S.; De Marco, T.; et al. Novel Wearable Seism cardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients. Circ. Heart Fail. 2018, 11, e004313. [CrossRef]
36. Katz, D.H.; Deo, R.C.; Aguilar, F.G.; Selvaraj, S.; Martinez, E.E.; Beussink-Nelson, L.; Kim, K.Y.A.; Peng, J.; Irvin, M.R.; Tiwari, H.; et al. Phen mapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction. J. Cardiovasc. Transl. Res. 2017, 10, 275–284. [CrossRef] [PubMed]
37. Acharya UR , Oh SL , Hagiwara Y , Tan  JH , Adam  M , Gertych  A, Tan  R. A deep convolutional neural network model to classify heartbeats. Comput Biol Med 2017;89:389–396.
38. Hannun   AY , Rajpurkar   P , Haghpanahi   M , Tison   GH , Bourn   C , Turakhia   MP , Ng   AY.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med  2019;25:65–69.
39. Al’Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, Pandey M,
    Maliakal G, van Rosendael AR, Beecy AN, Berman DS, Leipsic J, Nieman K,
    Andreini D, Pontone G, Schoepf UJ, Shaw LJ, Chang HJ, Narula J, Bax JJ, Guan
    Y, Min JK. Clinical applications of machine learning in cardiovascular disease and
    its relevance to cardiac imaging. Eur Heart J 2019;40:1975–1986.