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Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review)

  • Authors:
    • Ann Steffi Sharon John
    • Sriram Alagendran
    • Balamurugan Sivaprakasam
    • Mirudhula Kamakshi Mohan Ramaswamy
    • Karthick Selvaraj
    • Sharmila Ramanathan
    • Punitha Velam Chokkalingam
    • Nevetha Ravindran
    • Suvaithenamudhan Suvaiyarasan
  • View Affiliations / Copyright

    Affiliations: School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620024, India, Department of Computer Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology (SRMIST), Kattankulathur, Tamil Nadu 603203, India, Department of Bioinformatics, Bishop Heber College (Autonomous), Tiruchirappalli, Tamil Nadu 620017, India, Department of Anatomy, Meenakshi Medical College Hospital and Research Institute (MMCHRI), Kanchipuram, Tamil Nadu 631552, India, Department of Biotechnology, Bishop Heber College (Autonomous), Tiruchirappalli, Tamil Nadu 620017, India, Department of Community Medicine, Meenakshi Medical College Hospital and Research Institute, Kanchipuram, Tamil Nadu 631552, India, Department of Biotechnology, Cauvery College for Women (Autonomous), Tiruchirappalli, Tamil Nadu 620018, India, Central Research Laboratory, Meenakshi Academy of Higher Education and Research (MAHER), Chennai, Tamil Nadu 600078, India
    Copyright: © Sharon John et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].
  • Article Number: 75
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    Published online on: June 16, 2025
       https://doi.org/10.3892/wasj.2025.363
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Abstract

The incidence of cardiovascular disease (CVD) is rising steadily and continues to be the major cause of mortality worldwide. The pressing requirement is to develop personalised healthcare solutions. Digital twin (DT) and artificial intelligence (AI) technology can change the treatment of CV through personal disease modelling, risk stratification, diagnosis and prediction. AI‑powered DT technologies develop patient‑specific simulations that aid in early diagnosis, optimized treatment and post‑intervention monitoring. Machine learning algorithms and deep neural networks enable real‑time data identity from electronic health records, portable sensors and medical imaging to continuously update digital twins to represent physiological changes. AI‑powered DT models also help in better clinical decision‑making by modelling disease progression and accurately predicting treatment outcomes. However, its universal adoption is hampered by issues of data privacy concerns, computational power requirements, and regulatory compliance. Strengthening these capabilities using good data stewardship, interdisciplinarity and next‑generation computational architectures will accelerate the use of DT technology in cardiovascular medicine. The present review emphasizes the applications of AI‑based DT models to correct the future of accurate cardiology, pursue the patient's results and reduce the burden of health care.
View Figures

Figure 1

Major cardiovascular diseases.

Figure 2

Digital twin technology in
cardiovascular diseases. AI, artificial intelligence.

Figure 3

Applications of digital twin
technology in cardiovascular diseases.

Figure 4

Flowchart of AI-driven cardiac
digital twin development, from data collection to real-time
monitoring and decision support. CNNs, convolutional neural
networks; RNNs, recurrent neural networks.
View References

1 

Frąk W, Wojtasińska A, Lisińska W, Młynarska E, Franczyk B and Rysz J: Pathophysiology of cardiovascular diseases: New insights into molecular mechanisms of atherosclerosis, arterial hypertension, and coronary artery disease. Biomedicines. 10(1938)2022.PubMed/NCBI View Article : Google Scholar

2 

Yuyun MF, Sliwa K, Kengne AP, Mocumbi AO and Bukhman G: Cardiovascular diseases in sub-saharan africa compared to high-income countries: An epidemiological perspective. Glob Heart. 15(15)2020.PubMed/NCBI View Article : Google Scholar

3 

Thiriet M: Cardiovascular Disease: An Introduction. In: Vasculopathies. Biomathematical and Biomechanical Modeling of the Circulatory and Ventilatory Systems. Vol. 8. Springer, Cham, 2018.

4 

Mensah GA, Roth GA and Fuster V: The global burden of cardiovascular diseases and risk factors: 2020 and beyond. J Am Coll Cardiol. 74:2529–2532. 2019.PubMed/NCBI View Article : Google Scholar

5 

Gaziano T, Reddy KS, Paccaud F, Horton S and Chaturvedi V: Chapter 33 cardiovascular disease'. In Jamison DT, Breman J, Measham AR, Alleyne G, Claeson M, Evans DB, Jha P, Mills A and Musgrove P (eds). Disease control priorities in developing countries, 2nd edition. Washington: World Bank, pp645-662, 2006.

6 

Di Cesare M, Perel P, Taylor S, Kabudula C, Bixby H, Gaziano TA, McGhie DV, Mwangi J, Pervan B, Narula J, et al: The heart of the world. Glob Heart. 19(11)2024.PubMed/NCBI View Article : Google Scholar

7 

Global Cardiovascular Risk Consortium. Magnussen C, Ojeda FM, Leong DP, Alegre-Diaz J, Amouyel P, Aviles-Santa L, De Bacquer D, Ballantyne CM, Bernabé-Ortiz A, et al: Global effect of modifiable risk factors on cardiovascular disease and mortality. N Engl J Med. 389:1273–1285. 2023.PubMed/NCBI View Article : Google Scholar

8 

Lindstrom M, DeCleene N, Dorsey H, Fuster V, Johnson CO, LeGrand KE, Mensah GA, Razo C, Stark B, Varieur Turco J and Roth GA: Global burden of cardiovascular diseases and risks collaboration, 1990-2021. J Am Coll Cardiol. 80:2372–2425. 2022.PubMed/NCBI View Article : Google Scholar

9 

World Health Organization (WHO): The Global Health Observatory. Explore a World of Health Data. WHO, Geneva, 2021.

10 

Johansen H, Thillaiampalam S, Nguyen D and Sambell C: Diseases of the circulatory system-hospitalization and mortality. Health Rep. 17:49–53. 2005.PubMed/NCBI

11 

Saito I, Yamagishi K, Kokubo Y, Yatsuya H, Iso H, Sawada N, Inoue M and Tsugane S: Impact of cardiovascular disease on the death certificate diagnosis of heart failure, ischemic heart disease, and cerebrovascular disease-The Japan public health center-based prospective study. Circ J. 87:1196–1202. 2023.PubMed/NCBI View Article : Google Scholar

12 

Day IN and Wilson DI: Science, medicine, and the future: Genetics and cardiovascular risk. BMJ. 323:1409–1412. 2001.PubMed/NCBI View Article : Google Scholar

13 

Grejtakova D, Boronova I, Bernasovska J and Bellosta S: PCSK9 and lipid metabolism: Genetic variants, current therapies, and cardiovascular outcomes. Cardiovasc Drugs Ther: Jun 22, 2024 (Epub ahead of print).

14 

Coorey G, Figtree GA, Fletcher DF, Snelson VJ, Vernon ST, Winlaw D, Grieve SM, McEwan A, Yang JYH, Qian P, et al: The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field. NPJ Digit Med. 5(126)2022.PubMed/NCBI View Article : Google Scholar

15 

Moiseev VS, Demurov LM, Kobalava ZD, Chistiakov DA, Tereshchenko SN, Kondrat'ev II, Korovina EA and Nosikov VV: The polymorphism of the angiotensin-converting enzyme gene in patients with hypertension, left ventricular hypertrophy and the development of a myocardial infarct at a young age. Preliminary report. Ter Arkh. 69:18–23. 1997.PubMed/NCBI(In Russian).

16 

Sheikhy A, Fallahzadeh A, Aghaei Meybodi HR, Hasanzad M, Tajdini M and Hosseini K: Personalized medicine in cardiovascular disease: Review of literature. J Diabetes MetabDisord. 20:1793–1805. 2021.PubMed/NCBI View Article : Google Scholar

17 

Vallée A: Envisioning the future of personalized medicine: Role and realities of digital twins. J Med Internet Res. 26(e50204)2024.PubMed/NCBI View Article : Google Scholar

18 

Thangaraj PM, Benson SH, Oikonomou EK, Asselbergs FW and Khera R: Cardiovascular care with digital twin technology in the era of generative artificial intelligence. Eur Heart J. 45:4808–4821. 2024.PubMed/NCBI View Article : Google Scholar : (Epub ahead of print).

19 

Sel K, Osman D, Zare F, Masoumi Shahrbabak S, Brattain L, Hahn JO, Inan OT, Mukkamala R, Palmer J, Paydarfar D, et al: Building digital twins for cardiovascular health: From principles to clinical impact. J Am Heart Assoc. 13(e031981)2024.PubMed/NCBI View Article : Google Scholar

20 

Manocha A, Bhatia M and Kumar G: Smart monitoring solution for dengue infection control: A digital twin-inspired approach. Comput Methods Programs Biomed. 257(108459)2024.PubMed/NCBI View Article : Google Scholar

21 

Banerjee S, Das D, Chatterjee P and Ghosh U: Blockchain-enabled digital twin technology for next-generation transportation systems. In: 2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC). IEEE, pp224-229, 2023.

22 

Liu J, Zhang T, Fan J and Lang S: Applications of digital twin technology in AUV. In: 2023 IEEE 11th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, pp293-297, 2023.

23 

Botín-Sanabria DM, Mihaita AS, Peimbert-García RE, Ramírez-Moreno MA, Ramírez-Mendoza RA and Lozoya-Santos JDJ: Digital twin technology challenges and applications: A comprehensive review. Remote Sens. 14(1335)2022.

24 

Wagner T, Kittl C, Jakob J, Hiry J and Häger U: Digital twins in power systems: A proposal for a definition. IEEE Power Energy Mag. 22:16–23. 2024.

25 

Sado K, Peskar J, Downey A, Khan J and Booth K: A digital twin based forecasting framework for power flow management in DC microgrids. Sci Rep. 15(6430)2025.PubMed/NCBI View Article : Google Scholar

26 

Bahrin MAK, Othman MF, Azli NHN and Talib MF: Industry 4.0: A review on industrial automation and robotic. J Teknol. 78:137–143. 2016.

27 

Fuller A, Fan Z, Day C and Barlow C: Digital twin: Enabling technologies, challenges, and open research. IEEE Access. 8:108952–108971. 2020.

28 

De Benedictis A, Mazzocca N, Somma A and Strigaro C: Digital twins in healthcare: An architectural proposal and its application in a social distancing case study. IEEE J Biomed Health Inform. 27:5143–5154. 2023.PubMed/NCBI View Article : Google Scholar

29 

Sun T, He X and Li Z: Digital twin in healthcare: Recent updates and challenges. Digit Health. 9(20552076221149651)2023.PubMed/NCBI View Article : Google Scholar

30 

Vallée A: Digital twin for healthcare systems. Front Digit Health. 5(1253050)2023.PubMed/NCBI View Article : Google Scholar

31 

Ghatti S, Yurish LA, Shen H, Rheuban K, Enfield KB, Facteau NR, Engel G and Dowdell K: Digital twins in healthcare: A survey of current methods. Arch Clin Biomed Res. 7:365–338. 2023.

32 

Ying L, Zhang L, Yang Y, Zhou L, Ren L, Wang F, Liu R, Pang Z and Deen MJ: A novel cloud-based framework for elderly healthcare services using digital twin. IEEE Access. 7:49088–49101. 2019.

33 

Lehtola VV, Koeva M, Elberink SO, Raposo P, Virtanen JP, Vahdatikhaki F and Borsci S: Digital twin of a city: Review of technology serving city needs. Int J Appl Earth Obs Geoinf. 114(102915)2022.

34 

Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, et al: The ‘digital twin’ to enable the vision of precision cardiology. Eur Heart J. 41:4556–4564. 2020.PubMed/NCBI View Article : Google Scholar

35 

Gillette K, Gsell MAF, Prassl AJ, Karabelas E, Reiter U, Reiter G, Grandits T, Payer C, Štern D, Urschler M, et al: A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs. Med Image Anal. 71(102080)2021.PubMed/NCBI View Article : Google Scholar

36 

Lamata P: King's College London. Thousands of cardiac ‘digital twins’ offer new insights into the heart, 2025.

37 

Dorbala P and Iyer R: Carle Illinois College of Medicine. Digital twinning: New machine learning research tracks heart failure development for targeted treatment, 2024.

38 

Sakata K, Bradley RP, Prakosa A, Yamamoto CAP, Ali SY, Loeffler S, Tice BM, Boyle PM, Kholmovski EG, Yadav R, et al: Assessing the arrhythmogenic propensity of fibrotic substrate using digital twins to inform a mechanisms-based atrial fibrillation ablation strategy. Nat Cardiovasc Res. 3:857–868. 2024.PubMed/NCBI View Article : Google Scholar

39 

Rudnicka Z, Proniewska K, Perkins M and Pręgowska A: Cardiac healthcare digital twins supported by artificial intelligence-based algorithms and extended reality: A systematic review. Electronics. 13(866)2024.

40 

de Lepper AGW, Buck CMA, van 't Veer M, Huberts W, van de Vosse FN and Dekker LRC: From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy. J R Soc Interface. 19(20220317)2022.PubMed/NCBI View Article : Google Scholar

41 

Martin CH, Reventos-Presmanes J, Guichard JB, Mont L, Guillem MS, Climent AM and Hernandez I: Generation of cardiac digital twins based on noninvasive cardiac mapping. EP Europace. 25 (Suppl 1)(euad122.643)2023.

42 

Jones D, Snider C, Nassehi A, Yon J and Hicks B: Characterising the digital twin: a systematic literature review. CIRP J Manuf Sci Technol. 29:36–52. 2020.

43 

Katsoulakis E, Wang Q, Wu H, Shahriyari L, Fletcher R, Liu J, Achenie L, Liu H, Jackson P, Xiao Y, et al: Digital twins for health: A scoping review. NPJ Digit Med. 7(77)2024.PubMed/NCBI View Article : Google Scholar

44 

Papachristou K, Katsakiori PF, Papadimitroulas P, Strigari L and Kagadis GC: Digital twins' advancements and applications in healthcare, towards precision medicine. J Pers Med. 14(1101)2024.PubMed/NCBI View Article : Google Scholar

45 

Lim KYH, Zheng P and Chen CH: A state-of-the-art survey of digital twin: Techniques, engineering product lifecycle management and business innovation perspectives. J Intell Manuf. 31:1313–1337. 2019.

46 

Chakshu NK, Sazonov I and Nithiarasu P: Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis. Biomech Model Mechanobiol. 20:449–465. 2021.PubMed/NCBI View Article : Google Scholar

47 

Roney CH, Sim I, Yu J, Beach M, Mehta A, Alonso Solis-Lemus J, Kotadia I, Whitaker J, Corrado C, Razeghi O, et al: Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models. Circ Arrhythm Electrophysiol. 15(010253)2022.PubMed/NCBI View Article : Google Scholar

48 

Viceconti M, De Vos M, Mellone S and Geris L: Position paper From the digital twins in healthcare to the virtual human twin: A moon-shot project for digital health research. IEEE J Biomed Health Inform: Oct 11, 2023 (Epub ahead of print).

49 

Zheng T, Azzolin L, Sánchez J, Dössel O and Loewe A: An automated pipeline for generating fiber orientation and region annotation in patient-specific atrial models. Curr Dir Biomed Eng. 7:136–139. 2021.

50 

Fathima SN: An update on myocardial infarction. Curr Res Trends Med Sci Technol. 1(95)2021.

51 

Thygesen K, Alpert JS, Jaffe AS, Simoons ML, Chaitman BR and White HD: Writing Group on behalf of the Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarction. Third universal definition of myocardial infarction. Glob Heart. 7:275–295. 2012.PubMed/NCBI View Article : Google Scholar

52 

Li L, Camps J, Jenny Wang Z, Beetz M, Banerjee A, Rodriguez B and Grau V: Toward enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference. IEEE Trans Med Imaging. 43:2466–2478. 2024.PubMed/NCBI View Article : Google Scholar

53 

Fu DG: Cardiac arrhythmias: Diagnosis, symptoms, and treatments. Cell Biochem Biophys. 73:291–296. 2015.PubMed/NCBI View Article : Google Scholar

54 

Deng D, Arevalo HJ, Prakosa A, Callans DJ and Trayanova NA: A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction. EP Europace. 18 (Suppl 4):iv60–iv66. 2016.PubMed/NCBI View Article : Google Scholar

55 

Arevalo HJ, Vadakkumpadan F, Guallar E, Jebb A, Malamas P, Wu KC and Trayanova NA: Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat Commun. 7(11437)2016.PubMed/NCBI View Article : Google Scholar

56 

Ahmadova AA: Applications of digital twins in medicine and the ontological model of medical digital twins. Probl Inf Soc. 15:98–105. 2024.

57 

Hu Y, Chen J, Hu L, Li D, Yan J, Ying H, Liang H and Wu J: Personalized heart disease detection via ECG digital twin generation. arXiv [Preprint]: 11171, 2024.

58 

Moore JH, Li X, Chang JH, Tatonetti NP, Theodorescu D, Chen Y, Asselbergs FW, Venkatesan M and Wang ZP: SynTwin: A graph-based approach for predicting clinical outcomes using digital twins derived from synthetic patients. Pac Symp Biocomput. 29:96–107. 2024.PubMed/NCBI

59 

Joshi S, Dharmalingam M, Vadavi A, Thajudeen M, Keshavamurthy A, Bhonsley A and Shamanna P: Abstract P278: 1-year outcomes of A1c reduction, weight loss, and lowered QRISK3 scores in type 2 diabetes remission: Insights from an RCCT leveraging whole-body digital twin technology. Circulation. 149 (Suppl 1)(P278)2024.

60 

Hwang T, Kwon O, Lim B, Jin Z, Yang S, Kim D, Park J, Yu H, Kim T, Uhm J, et al: Clinical application of virtual antiarrhythmic drug test using digital twins in patients who recurred atrial fibrillation after catheter ablation. EP Europace. 25(euad122.076)2023.

61 

Kadry K, Gupta S, Nezami FR and Edelman ER: Probing the limits and capabilities of diffusion models for the anatomic editing of digital twins. NPJ Digit Med. 7(354)2024.PubMed/NCBI View Article : Google Scholar

62 

Winter PD and Chico TJA: Using the non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework to identify barriers and facilitators for the implementation of digital twins in cardiovascular medicine. Sensors (Basel). 23(6333)2023.PubMed/NCBI View Article : Google Scholar

63 

Lareyre F, Adam C, Carrier M and Raffort J: Using digital twins for precision medicine in vascular surgery. Ann Vasc Surg. 67:e577–e578. 2020.PubMed/NCBI View Article : Google Scholar

64 

Erol T, Mendi AF and Doğan D: The digital twin revolution in healthcare, 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); Istanbul, Turkey. IEEE, pp1-7, 2020.

65 

Shu H, Liang R, Li Z, Goodridge A, Zhang X, Ding H, Nagururu N, Sahu M, Creighton FX, Taylor RH, et al: Twin-S: A digital twin for skull base surgery. Int J Comput Assist Radiol Surg. 18:1077–1084. 2023.PubMed/NCBI View Article : Google Scholar

66 

An Q, Rahman S, Zhou J and Kang JJ: A comprehensive review on machine learning in healthcare industry: classification, restrictions, opportunities and challenges. Sensors (Basel). 23(4178)2023.PubMed/NCBI View Article : Google Scholar

67 

Rathore MM, Shah SA, Shukla D, Bentafat E and Bakiras S: The role of AI, machine learning, and big data in digital twinning: A systematic literature review, challenges, and opportunities. IEEE Access. 9:32030–32052. 2021.

68 

Bezborodova OE, Bodin ON, Gerasimov AI, Kramm MN, Rahmatullov RF and Ubiennykh AG: ‘Digital Twin’ technology in medical information systems. J Phys Conf Ser. 1515(052022)2020.

69 

Vinuesa R and Brunton SL: Enhancing computational fluid dynamics with machine learning. Nat Comput Sci. 2:358–366. 2022.PubMed/NCBI View Article : Google Scholar

70 

Cuocolo R, Caruso M, Perillo T, Ugga L and Petretta M: Machine learning in oncology: A clinical appraisal. Cancer Lett. 481:55–62. 2020.PubMed/NCBI View Article : Google Scholar

71 

Krittanawong C, Virk HUH, Bangalore S, Wang Z, Johnson KW, Pinotti R, Zhang H, Kaplin S, Narasimhan B, Kitai T, et al: Machine learning prediction in cardiovascular diseases: A meta-analysis. Sci Rep. 10(16057)2020.PubMed/NCBI View Article : Google Scholar

72 

Dalal S, Goel P, Onyema E, Alharbi A, Mahmoud A, Algarni M and Awal H: Application of machine learning for cardiovascular disease risk prediction. Comput Intell Neurosci. 2023(9418666)2023.

73 

Yarasuri VK, Reddy DS, Muneesh PS, Kaushik RVS, Vardhan TN and Nisha KL: Developing machine learning models for cardiovascular disease prediction. 2022 2nd Asian Conference on Innovation in Technology (ASIANCON). IEEE, pp1-6, 2022.

74 

Brites ISG, da Silva LM, Barbosa JLV, Rigo SJ, Correia SD and Leithardt VRQ: Machine learning and IoT applied to cardiovascular diseases identification through heart sounds: A literature review. Informatics. 8(73)2021.

75 

Zhou J, You D, Bai J, Chen X, Wu Y, Wang Z, Tang Y, Zhao Y and Feng G: Machine learning methods in real-world studies of cardiovascular disease. Cardiovasc Innov Appl. 7(25)2023.

76 

Bzdok D, Krzywinski M and Altman N: Machine learning: Supervised methods. Nat Methods. 15:5–6. 2018.

77 

Baessler B, Mannil M, Oebel S, Maintz D, Alkadhi H and Manka R: Subacute and chronic left ventricular myocardial scar: Accuracy of texture analysis on nonenhanced cine MR images. Radiology. 286:103–112. 2018.PubMed/NCBI View Article : Google Scholar

78 

Ali MM, Paul BK, Ahmed K, Bui FM, Quinn JMW and Moni MA: Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Comput Biol Med. 136(104672)2021.PubMed/NCBI View Article : Google Scholar

79 

Naeem S, Ali A, Anam S and Ahmed MM: An unsupervised machine learning algorithm: Comprehensive review. Int J Com Dig Sys. 13:911–921. 2023.

80 

Gosling RC, Morris PD, Silva Soto DA, Lawford PV, Hose DR and Gunn JP: Virtual coronary intervention: A treatment planning tool based upon the angiogram. JACC Cardiovasc Imaging. 12:865–872. 2019.PubMed/NCBI View Article : Google Scholar

81 

Usmani UA, Happonen A and Watada J: A review of unsupervised machine learning frameworks for anomaly detection in industrial applications. In: Arai K (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems. Vol. 507. Springer, Cham, pp158-189, 2022.

82 

Cholevas C, Angeli E, Sereti Z, Mavrikos E and Tsekouras GE: Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A survey. Algorithms. 17(201)2024.

83 

Thill M, Konen W, Wang H and Bäck T: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Appl Soft Comput. 112(107751)2021.

84 

Nanehkaran YA, Licai Z, Chen J, Jamel AAM, Shengnan Z, Navaei YD and Aghbolagh MA: Anomaly detection in heart disease using a density-based unsupervised approach. Wirel Commun Mob Comput. 2022(6913043)2022.

85 

Nakao T, Hanaoka S, Nomura Y, Murata M, Takenaga T, Miki S, Watadani T, Yoshikawa T, Hayashi N and Abe O: Unsupervised deep anomaly detection in chest radiographs. J Digit Imaging. 34:418–427. 2021.PubMed/NCBI View Article : Google Scholar

86 

Flores AM, Schuler A, Eberhard AV, Olin JW, Cooke JP, Leeper NJ, Shah NH and Ross EG: Unsupervised learning for automated detection of coronary artery disease subgroups. J Am Heart Assoc. 10(e021976)2021.PubMed/NCBI View Article : Google Scholar

87 

Manakitsa N, Maraslidis GS, Moysis L and Fragulis GF: A review of machine learning and deep learning for object detection, semantic segmentation, and human action recognition in machine and robotic vision. Technologies. 12(15)2024.

88 

Fan J, Ma C and Zhong Y: A selective overview of deep learning. Stat Sci. 36:264–290. 2021.PubMed/NCBI View Article : Google Scholar

89 

Shrestha A and Mahmood A: Review of deep learning algorithms and architectures. IEEE Access. 7:53040–53065. 2019.

90 

Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B and Yang GZ: Deep learning for health informatics. IEEE J Biomed Health Inform. 21:4–21. 2017.PubMed/NCBI View Article : Google Scholar

91 

Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J and Socher R: Deep learning-enabled medical computer vision. NPJ Digit Med. 4(5)2021.PubMed/NCBI View Article : Google Scholar

92 

Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W and Rueckert D: Deep learning for cardiac image segmentation: A review. Front Cardiovasc Med. 7(25)2020.PubMed/NCBI View Article : Google Scholar

93 

Subramani S, Varshney N, Anand MV, Soudagar MEM, Al-Keridis LA, Upadhyay TK, Alshammari N, Saeed M, Subramanian K, Anbarasu K and Rohini K: Cardiovascular diseases prediction by machine learning incorporation with deep learning. Front Med (Lausanne). 10(1150933)2023.PubMed/NCBI View Article : Google Scholar

94 

Xia B, Innab N, Kandasamy V, Ahmadian A and Ferrara M: Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization. Sci Rep. 14(21777)2024.PubMed/NCBI View Article : Google Scholar

95 

Shahul Hameed MA, Qureshi AM and Kaushik A: Bias mitigation via synthetic data generation: A review. Electronics. 13(3909)2024.

96 

Liu T, Tian Y, Zhao S, Huang X and Wang Q: Residual convolutional neural network for cardiac image segmentation and heart disease diagnosis. IEEE Access. 8:82153–8216. 2020.

97 

Hu H, Fang B, Ran Y, Wei X, Xian W, Zhou M and Kwong S: Deep dual-stream convolutional neural networks for cardiac image semantic segmentation. IEEE Trans Industr Inform. 20:7440–7448. 2024.

98 

Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, Lee AM, Aung N, Lukaschuk E, Sanghvi MM, et al: Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 20(65)2018.PubMed/NCBI View Article : Google Scholar

99 

Romaguera LV, Romero FP, Costa Filho CFF and Costa MGF: Myocardial segmentation in cardiac magnetic resonance images using fully convolutional neural networks. Biomed Signal Process Control. 44:48–57. 2018.

100 

Liu D, Jia Z, Jin M, Liu Q, Liao Z, Zhong J, Ye H and Chen G: Cardiac magnetic resonance image segmentation based on convolutional neural network. Comput Methods Programs Biomed. 197(105755)2020.PubMed/NCBI View Article : Google Scholar

101 

Zotti C, Luo Z, Lalande A and Jodoin P: Convolutional neural network with shape prior applied to cardiac MRI segmentation. IEEE J Biomed Health Inform. 23:1119–1128. 2019.PubMed/NCBI View Article : Google Scholar

102 

Mienye ID, Swart TG and Obaido G: Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information. 15(517)2024.

103 

Baruah RD and Organero MM: Explicit context integrated recurrent neural network for applications in smart environments. Expert Syst Appl. 255(124752)2024.

104 

Choi E, Schuetz A, Stewart WF and Sun J: Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 24:361–370. 2017.PubMed/NCBI View Article : Google Scholar

105 

Lu XH, Liu A, Fuh SC, Lian Y, Guo L, Yang Y, Marelli A and Li Y: Recurrent disease progression networks for modelling risk trajectory of heart failure. PLoS One. 16(e0245177)2021.PubMed/NCBI View Article : Google Scholar

106 

Rasmy L, Wu Y, Wang N, Geng X, Zheng WJ, Wang F, Wu H, Xu H and Zhi D: A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J Biomed Inform. 84:11–16. 2018.PubMed/NCBI View Article : Google Scholar

107 

Shahi S, Marcotte CD, Herndon CJ, Fenton FH, Shiferaw Y and Cherry EM: Long-time prediction of arrhythmic cardiac action potentials using recurrent neural networks and reservoir computing. Front Physiol. 12(734178)2021.PubMed/NCBI View Article : Google Scholar

108 

Lynn HM, Pan SB and Kim P: A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks. IEEE Access. 7:145395–145405. 2019.

109 

Jovanovic L, Zivkovic M, Bacanin N, Bozovic A, Bisevac P and Antonijevic M: Metaheuristic optimized electrocardiography time-series anomaly classification with recurrent and long-short term neural networks. Int J Hybrid Intell Syst. 20:275–300. 2024.

110 

Łukaniszyn M, Majka Ł, Grochowicz B, Mikołajewski D and Kawala-Sterniuk A: Digital twins generated by artificial intelligence in personalized healthcare. Appl Sci. 14(9404)2024.

111 

Vallée A: Challenges and directions for digital twin implementation in otorhinolaryngology. Eur Arch Otorhinolaryngol. 281:6155–6159. 2024.PubMed/NCBI View Article : Google Scholar

112 

Vidovszky AA, Fisher CK, Loukianov AD, Smith AM, Tramel EW, Walsh JR and Ross JL: Increasing acceptance of AI-generated digital twins through clinical trial applications. Clin Transl Sci. 17(e13897)2024.PubMed/NCBI View Article : Google Scholar

113 

Meijer C, Uh HW and El Bouhaddani S: Digital twins in healthcare: Methodological challenges and opportunities. J Pers Med. 13(1522)2023.PubMed/NCBI View Article : Google Scholar

114 

Weerarathna IN, Kumar P, Verma P, Raymond D, Luharia A and Mishra G: Leveraging digital twin technology to combat cardiovascular disease: A comprehensive review. In: Proceedings of the 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, Wardha, pp1-6, 2024.

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Spandidos Publications style
Sharon John A, Alagendran S, Sivaprakasam B, Mohan Ramaswamy M, Selvaraj K, Ramanathan S, Velam Chokkalingam P, Ravindran N and Suvaiyarasan S: Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review). World Acad Sci J 7: 75, 2025.
APA
Sharon John, A., Alagendran, S., Sivaprakasam, B., Mohan Ramaswamy, M., Selvaraj, K., Ramanathan, S. ... Suvaiyarasan, S. (2025). Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review). World Academy of Sciences Journal, 7, 75. https://doi.org/10.3892/wasj.2025.363
MLA
Sharon John, A., Alagendran, S., Sivaprakasam, B., Mohan Ramaswamy, M., Selvaraj, K., Ramanathan, S., Velam Chokkalingam, P., Ravindran, N., Suvaiyarasan, S."Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review)". World Academy of Sciences Journal 7.4 (2025): 75.
Chicago
Sharon John, A., Alagendran, S., Sivaprakasam, B., Mohan Ramaswamy, M., Selvaraj, K., Ramanathan, S., Velam Chokkalingam, P., Ravindran, N., Suvaiyarasan, S."Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review)". World Academy of Sciences Journal 7, no. 4 (2025): 75. https://doi.org/10.3892/wasj.2025.363
Copy and paste a formatted citation
x
Spandidos Publications style
Sharon John A, Alagendran S, Sivaprakasam B, Mohan Ramaswamy M, Selvaraj K, Ramanathan S, Velam Chokkalingam P, Ravindran N and Suvaiyarasan S: Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review). World Acad Sci J 7: 75, 2025.
APA
Sharon John, A., Alagendran, S., Sivaprakasam, B., Mohan Ramaswamy, M., Selvaraj, K., Ramanathan, S. ... Suvaiyarasan, S. (2025). Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review). World Academy of Sciences Journal, 7, 75. https://doi.org/10.3892/wasj.2025.363
MLA
Sharon John, A., Alagendran, S., Sivaprakasam, B., Mohan Ramaswamy, M., Selvaraj, K., Ramanathan, S., Velam Chokkalingam, P., Ravindran, N., Suvaiyarasan, S."Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review)". World Academy of Sciences Journal 7.4 (2025): 75.
Chicago
Sharon John, A., Alagendran, S., Sivaprakasam, B., Mohan Ramaswamy, M., Selvaraj, K., Ramanathan, S., Velam Chokkalingam, P., Ravindran, N., Suvaiyarasan, S."Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review)". World Academy of Sciences Journal 7, no. 4 (2025): 75. https://doi.org/10.3892/wasj.2025.363
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