Skip to main content
SpringerLink
Log in

Application of Artificial Intelligence in Acute Coronary Syndrome: A Brief Literature Review

  • Review
  • Published:
Advances in Therapy Aims and scope Submit manuscript

Abstract

Artificial intelligence (AI) is defined as a set of algorithms and intelligence to try to imitate human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques. The application of AI in healthcare systems including hospitals and clinics has many possible advantages and future prospects. Applications of AI in cardiovascular medicine are machine learning techniques for diagnostic procedures including imaging modalities and biomarkers and predictive analytics for personalized therapies and improved outcomes. In cardiovascular medicine, AI-based systems have found new applications in risk prediction for cardiovascular diseases, in cardiovascular imaging, in predicting outcomes after revascularization procedures, and in newer drug targets. AI such as machine learning has partially resolved and provided possible solutions to unmet requirements in interventional cardiology. Predicting economically vital endpoints, predictive models with a wide range of health factors including comorbidities, socioeconomic factors, and angiographic factors comprising of the size of stents, the volume of contrast agent which was infused during angiography, stent malposition, and so on have been possible owing to machine learning and AI. Nowadays, machine learning techniques might possibly help in the identification of patients at risk, with higher morbidity and mortality following acute coronary syndrome (ACS). AI through machine learning has shown several potential benefits in patients with ACS. From diagnosis to treatment effects to predicting adverse events and mortality in patients with ACS, machine learning should find an essential place in clinical medicine and in interventional cardiology for the treatment and management of patients with ACS. This paper is a review of the literature which will focus on the application of AI in ACS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Trolice MP, Curchoe C, Quaas AM. Artificial intelligence-the future is now. J Assist Reprod Genet. 2021;38:1607–12.

  2. Mirbabaie M, Hofeditz L, Frick NRJ, Stieglitz S. Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research. AI Soc. 2021;1–22. https://doi.org/10.1007/s00146-021-01239-4.

  3. Leite ML, de Costa LSL, Cunha VA, Kreniski V, de Filho MOB, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today. 2021;S1359–6446(21):00308–11.

    Google Scholar 

  4. Piironen M, Ukkola O, Huikuri H, et al. Trends in long-term prognosis after acute coronary syndrome. Eur J Prev Cardiol. 2017;24(3):274–80.

    Article  Google Scholar 

  5. Bundhun PK, Sookharee Y, Bholee A, Huang F. Application of the SYNTAX score in interventional cardiology: a systematic review and meta-analysis. Medicine (Baltimore). 2017;96(28): e7410.

    Article  Google Scholar 

  6. Junior AMDSC, Pereira LGDS, de Souza TMB, et al. Prognostic accuracy of the GRACE score in octogenarians and nonagenarians with acute coronary syndromes. Arq Bras Cardiol. 2018;110(1):24–9.

    Google Scholar 

  7. de Carvalho LSF, Gioppato S, Fernandez MD, et al. Machine learning improves the identification of individuals with higher morbidity and avoidable health costs after acute coronary syndromes. Value Health. 2020;23(12):1570–9.

    Article  Google Scholar 

  8. Claeys MJ, Beauloye C, Pourbaix S, Sinnaeve PR, Rewinder Study Group. Real world insights on the initiation and treatment duration of oral antiplatelets in acute coronary syndromes: a retrospective cohort study. Eur Heart J Cardiovasc Pharmacother. 2017;3(4):189–97.

    PubMed  PubMed Central  Google Scholar 

  9. Al-Zaiti S, Besomi L, Bouzid Z, et al. Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram. Nat Commun. 2020;11(1):3966.

    Article  CAS  Google Scholar 

  10. Eberhard M, Nadarevic T, Cousin A, et al. Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience. Cardiovasc Diagn Ther. 2020;10(4):820–30.

    Article  Google Scholar 

  11. Than MP, Pickering JW, Sandoval Y, et al. Machine learning to predict the likelihood of acute myocardial infarction. Circulation. 2019;140(11):899–909.

    Article  Google Scholar 

  12. Baxt WG, Shofer FS, Sites FD, Hollander JE. A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain. Ann Emerg Med. 2002;40(6):575–83.

    Article  Google Scholar 

  13. Wu C-C, Hsu W-D, Islam MM, et al. An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain. Comput Methods Programs Biomed. 2019;173:109–17.

    Article  Google Scholar 

  14. Berikol GB, Yildiz O, Özcan IT. Diagnosis of acute coronary syndrome with a support vector machine. J Med Syst. 2016;40(4):84.

    Article  Google Scholar 

  15. Iannattone PA, Zhao X, VanHouten J, Garg A, Huynh T. Artificial intelligence for diagnosis of acute coronary syndromes: a meta-analysis of machine learning approaches. Can J Cardiol. 2020;36(4):577–83.

    Article  Google Scholar 

  16. Noh Y-K, Park JY, Choi BG, Kim K-E, Rha S-W. A machine learning-based approach for the prediction of acute coronary syndrome requiring revascularization. J Med Syst. 2019;43(8):253.

    Article  Google Scholar 

  17. Kulkarni H, Amin AP. Artificial intelligence in percutaneous coronary intervention: improved prediction of PCI-related complications using an artificial neural network. BMJ Innov. 2021. https://doi.org/10.1101/2020.08.17.20177055.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zack CJ, Senecal C, Kinar Y, et al. Leveraging machine learning techniques to forecast patient prognosis after percutaneous coronary intervention. JACC Cardiovasc Interv. 2019;12(14):1304–11.

    Article  Google Scholar 

  19. Chu J, Dong W, Wang J, He K, Huang Z. Treatment effect prediction with adversarial deep learning using electronic health records. BMC Med Inform Decis Mak. 2020;20(Suppl 4):139.

    Article  Google Scholar 

  20. Xue Y, Ziheng Hu, Jing Y, et al. Efficacy assessment of ticagrelor versus clopidogrel in Chinese patients with acute coronary syndrome undergoing percutaneous coronary intervention by data mining and machine-learning decision tree approaches. J Clin Pharm Ther. 2020;45(5):1076–86.

    Article  CAS  Google Scholar 

  21. Hernesniemi JA, Mahdiani S, Tynkkynen JA, et al. Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome—the MADDEC study. Ann Med. 2019;51(2):156–63.

    Article  Google Scholar 

  22. Pinaire J, Chabert E, Azé J, Bringay S, Landais P. Sequential pattern mining to predict medical in-hospital mortality from administrative data: application to acute coronary syndrome. J Healthc Eng. 2021;25(2021):5531807.

    Google Scholar 

  23. Hadanny A, Shouval R, Jianhua Wu, et al. Predicting 30-day mortality after ST elevation myocardial infarction: machine learning-based random forest and its external validation using two independent nationwide datasets. J Cardiol. 2021;S0914–5087(21):00152.

    Google Scholar 

  24. Duan H, Sun Z, Dong W, Huang Z. Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome. BMC Med Inform Decis Mak. 2019;19(1):5.

    Article  Google Scholar 

  25. Sherazi SWA, Bae J-W, Lee JY. A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome. PLoS ONE. 2021;16(6): e0249338.

    Article  CAS  Google Scholar 

  26. D’Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet. 2021;397(10270):199–207.

    Article  Google Scholar 

Download references

Acknowledgements

Disclosures

The authors Hong Wang, Quannan Zu, Jinglu Chen, Zhiren Yang and Mohammad Anis Ahmed declare that they have nothing to disclose.

Authors’ Contributions

Hong Wang, Quannan Zu, Jinglu Chen, Zhiren Yang and Mohammad Anis Ahmed were responsible for the conception and design, acquisition of data, analysis and interpretation of data, drafting the initial manuscript and revising it critically for important intellectual content. Dr Hong Wang wrote the final draft. All the authors approved the final manuscript as it has been written.

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Funding

This study was supported by Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project (S2017077) and the Guangxi Nanning Qingxiu District Science and Technology Development Project (Grant No. 2014S06). No Rapid Service Fee was received by the journal for the publication of this article.

Compliance with Ethics Guidelines

This literature review is based on previously published studies and does not contain any studies with human participants or animals performed by any of the authors.

Data Availability

This is a literature review. No data was used for any statistical analysis. All data shown in this literature review have been published and references have been provided throughout.

Author information

Authors and Affiliations

  1. Department of Cardiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, Guangxi, People’s Republic of China

    Hong Wang

  2. College of Management and Economics, Tianjin University, Tianjin, 300072, People’s Republic of China

    Quannan Zu & Jinglu Chen

  3. The State Key Laboratory Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Zhiren Yang

  4. Department of Cardiology, Peking University People’s Hospital, Beijing, People’s Republic of China

    Mohammad Anis Ahmed

Authors
  1. Hong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Quannan Zu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinglu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiren Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Anis Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Zu, Q., Chen, J. et al. Application of Artificial Intelligence in Acute Coronary Syndrome: A Brief Literature Review. Adv Ther 38, 5078–5086 (2021). https://doi.org/10.1007/s12325-021-01908-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12325-021-01908-2

Keywords

Search

Navigation