Skip to main content

Use of Artificial Intelligence in Cardiology: Where Are We in Africa?

  • Conference paper
  • First Online:
Towards new e-Infrastructure and e-Services for Developing Countries (AFRICOMM 2022)

Abstract

Cardiovascular diseases are the leading cause of death. Their inherent silent nature makes them often challenging to detect very early. The management of these diseases also requires many resources. Meanwhile, Artificial Intelligence (AI) in cardiology has recently showed its ability to fill the gap. Indeed, several scoring methods and prediction models have been developed to understand the different aspects of these pathologies. The purpose of this paper is to review the state-of-the-art of the use of AI and digital technologies in cardiology in developing countries and to see the place of Africa. We have conducted a bibliometric analysis with 222 papers and an in-depth study on 26 papers using real and local databases. The words arrhythmia, cardiovascular disease, deep learning, and machine learning come up most often. Support vector machine algorithms, decision tree-based assemblers, and convolutional neural networks are more used. Among the 26 papers studied, only one comes from Africa, 24 from Asia, and one is a joint work between researches from Uganda and Brazil. The results show that countries using these AI-based methods often have accessible health databases, and collaborations between health specialists and universities are frequent. The finding of the African studies is that they focused, in most instances, on medical research to find risk factors or statistics on the epidemiology of heart disease.

We are grateful to AFD for funding this research work. We would also like to thank ACE-SMIA, ACE-MITIC and DSTN for their support and the members of the AI4CARDIO project for their helpful suggestions and remarks to improve this work.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.donneesmondiales.com/pays-voie-developpement.php.

References

  1. Adoukonou, T.A., et al.: Prise en charge des accidents vasculaires cérébraux en Afrique subsaharienne. Revue Neurologique 166(11), 882–893 (2010). https://doi.org/10.1016/j.neurol.2010.06.004

    Article  Google Scholar 

  2. Ahsan, M.M., Siddique, Z.: Machine learning-based heart disease diagnosis: a systematic literature review. Artif. Intell. Med. 128, 102289 (2022). https://doi.org/10.1016/j.artmed.2022.102289

    Article  Google Scholar 

  3. Al-Absi, H.R.H., Refaee, M.A., Rehman, A.U., Islam, M.T., Belhaouari, S.B., Alam, T.: Risk factors and comorbidities associated to cardiovascular disease in Qatar: a machine learning based case-control study. IEEE Access 9, 29929–29941 (2021). https://doi.org/10.1109/ACCESS.2021.3059469

    Article  Google Scholar 

  4. Bonny, A., et al.: Cardiac arrhythmias in Africa: epidemiology, management challenges, and perspectives. J. Am. Coll. Cardiol. 73(1), 100–109 (2019). https://doi.org/10.1016/j.jacc.2018.09.084

    Article  Google Scholar 

  5. Cao, Z., et al.: Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients. Hum. Brain Mapp. 43(10), 3023–3036 (2022). https://doi.org/10.1002/hbm.25845

    Article  Google Scholar 

  6. Cardiovascular diseases (CVDs). https://www.who.int/fr/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

  7. Chakraborty, A., Sadhukhan, D., Pal, S., Mitra, M.: Automated myocardial infarction identification based on interbeat variability analysis of the photoplethysmographic data. Biomed. Sig. Process. Control 57, 101747 (2020). https://doi.org/10.1016/j.bspc.2019.101747

    Article  Google Scholar 

  8. Chen, H.Y., et al.: Artificial intelligence-enabled electrocardiography predicts left ventricular dysfunction and future cardiovascular outcomes: a retrospective analysis. J. Personalized Med. 12(3), 455 (2022). https://doi.org/10.3390/jpm12030455

    Article  MathSciNet  Google Scholar 

  9. Chen, J., Gao, Y.: The role of deep learning-based echocardiography in the diagnosis and evaluation of the effects of routine anti-heart-failure Western medicines in elderly patients with acute left heart failure. J. Healthc. Eng. 2021, 4845792 (2021). https://doi.org/10.1155/2021/4845792

    Article  Google Scholar 

  10. Chen, T.M., Huang, C.H., Shih, E.S., Hu, Y.F., Hwang, M.J.: Detection and classification of cardiac arrhythmias by a challenge-best deep learning neural network model. iScience 23(3), 100886 (2020). https://doi.org/10.1016/j.isci.2020.100886

    Article  Google Scholar 

  11. Chun, M., et al.: Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults. J. Am. Med. Inform. Assoc. JAMIA 28(8), 1719–1727 (2021). https://doi.org/10.1093/jamia/ocab068

    Article  Google Scholar 

  12. DeepECG4U: intelligence artificielle au service de la santé cardiaque. Site Web IRD

    Google Scholar 

  13. Dhananjay, B., Sivaraman, J.: Analysis and classification of heart rate using CatBoost feature ranking model. Biomed. Sig. Process. Control 68, 102610 (2021). https://doi.org/10.1016/j.bspc.2021.102610

    Article  Google Scholar 

  14. Diao, M., et al.: Cardiopathies rhumatismales évolutives a propos de 17 cas colligés au chu de Dakar. Undefined (2005)

    Google Scholar 

  15. Gautam, A., Raman, B.: Towards effective classification of brain hemorrhagic and ischemic stroke using CNN. Biomed. Sig. Process. Control 63, 102178 (2021). https://doi.org/10.1016/j.bspc.2020.102178

    Article  Google Scholar 

  16. Jana, B., Oswal, K., Mitra, S., Saha, G., Banerjee, S.: Detection of peripheral arterial disease using Doppler spectrogram based expert system for Point-of-Care applications. Biomed. Sig. Process. Control 54, 101599 (2019). https://doi.org/10.1016/j.bspc.2019.101599

    Article  Google Scholar 

  17. Ju, C., et al.: Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach. ESC Heart Fail. 8(4), 2837–2845 (2021). https://doi.org/10.1002/ehf2.13358

    Article  Google Scholar 

  18. Kimani, K., Namukwaya, E., Grant, L., Murray, S.A.: What is known about heart failure in sub-Saharan Africa: a scoping review of the English literature. BMJ Support. Palliat. Care 7(2), 122–127 (2017). https://doi.org/10.1136/bmjspcare-2015-000924

    Article  Google Scholar 

  19. Kwon, J.M., et al.: Artificial intelligence algorithm for predicting mortality of patients with acute heart failure. PLoS ONE 14(7), e0219302 (2019). https://doi.org/10.1371/journal.pone.0219302

    Article  Google Scholar 

  20. Li, X., Bian, D., Yu, J., Li, M., Zhao, D.: Using machine learning models to improve stroke risk level classification methods of China national stroke screening. BMC Med. Inform. Decis. Making 19, 261 (2019). https://doi.org/10.1186/s12911-019-0998-2

    Article  Google Scholar 

  21. Li, Y.H., Lee, I.T., Chen, Y.W., Lin, Y.K., Liu, Y.H., Lai, F.P.: Using text content from coronary catheterization reports to predict 5-year mortality among patients undergoing coronary angiography: a deep learning approach. Front. Cardiovasc. Med. 9, 800864 (2022). https://doi.org/10.3389/fcvm.2022.800864

    Article  Google Scholar 

  22. Martins, J.F.B.S., et al.: Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning. J. Am. Med. Inform. Assoc. JAMIA 28(9), 1834–1842 (2021). https://doi.org/10.1093/jamia/ocab061

    Article  Google Scholar 

  23. Mendez, G.F., Cowie, M.R.: The epidemiological features of heart failure in developing countries: a review of the literature. Int. J. Cardiol. 80(2–3), 213–219 (2001). https://doi.org/10.1016/S0167-5273(01)00497-1

    Article  Google Scholar 

  24. Mocumbi, A.O.H., Ferreira, M.B.: Neglected cardiovascular diseases in Africa. J. Am. Coll. Cardiol. 55(7), 680–687 (2010). https://doi.org/10.1016/j.jacc.2009.09.041

    Article  Google Scholar 

  25. Mohammadi, F., Sheikhani, A., Razzazi, F., Ghorbani Sharif, A.: Non-invasive localization of the ectopic foci of focal atrial tachycardia by using ECG signal based sparse decomposition algorithm. Biomed. Sig. Process. Control 70, 103014 (2021). https://doi.org/10.1016/j.bspc.2021.103014

    Article  Google Scholar 

  26. Mohammed, E.M., Alnory, A.: Bivariate analysis of cardiovascular disease risk factors in Gezira state, Sudan (2019): a hospital-based case-control study. In: 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan, pp. 1–6. IEEE, February 2021. https://doi.org/10.1109/ICCCEEE49695.2021.9429659

  27. Mosca, L., Barrett-Connor, E., Wenger, N.K.: Sex/gender differences in cardiovascular disease prevention: what a difference a decade makes. Circulation 124(19), 2145–2154 (2011). https://doi.org/10.1161/CIRCULATIONAHA.110.968792

    Article  Google Scholar 

  28. Nayak, S.K., Pradhan, B.K., Banerjee, I., Pal, K.: Analysis of heart rate variability to understand the effect of cannabis consumption on Indian male paddy-field workers. Biomed. Sig. Process. Control 62, 102072 (2020). https://doi.org/10.1016/j.bspc.2020.102072

    Article  Google Scholar 

  29. Peng, J., et al.: Research on application of data mining algorithm in cardiac medical diagnosis system. Biomed. Res. Int. 2022, 7262010 (2022). https://doi.org/10.1155/2022/7262010

    Article  Google Scholar 

  30. Prévention des maladies cardiovasculaires: Guide de poche pour l’évaluation et la prise en charge du risque cardiovasculaire (diagrammes OMS/ISH de prédiction du risque cardiovasculaire pour la sous-région africaine de l’OMS AFR D, AFR E). https://apps.who.int/iris/handle/10665/43848

  31. Reychav, I., Zhu, L., McHaney, R., Arbel, Y.: Empirical thresholding logistic regression model based on unbalanced cardiac patient data. Procedia Comput. Sci. 121, 160–165 (2017). https://doi.org/10.1016/j.procs.2017.11.022

    Article  Google Scholar 

  32. Rezaee, M., Putrenko, I., Takeh, A., Ganna, A., Ingelsson, E.: Development and validation of risk prediction models for multiple cardiovascular diseases and Type 2 diabetes. PLoS ONE 15(7), e0235758 (2020). https://doi.org/10.1371/journal.pone.0235758

    Article  Google Scholar 

  33. Salah, I.B., De la Rosa, R., Ouni, K., Salah, R.B.: Automatic diagnosis of valvular heart diseases by impedance cardiography signal processing. Biomed. Sig. Process. Control 57, 101758 (2020). https://doi.org/10.1016/j.bspc.2019.101758

    Article  Google Scholar 

  34. Sène Diouf, F., et al.: Survie des accidents vasculaires cérébraux comateux à Dakar (Sénégal). Revue Neurologique 164(5), 452–458 (2008). https://doi.org/10.1016/j.neurol.2008.01.007

    Article  Google Scholar 

  35. Shirole, U., Joshi, M., Bagul, P.: Cardiac, diabetic and normal subjects classification using decision tree and result confirmation through orthostatic stress index. Inform. Med. Unlocked 17, 100252 (2019). https://doi.org/10.1016/j.imu.2019.100252

    Article  Google Scholar 

  36. Sparapani, R., et al.: Detection of left ventricular hypertrophy using Bayesian additive regression trees: the MESA. J. Am. Heart Assoc. 8(5), e009959 (2019). https://doi.org/10.1161/JAHA.118.009959

    Article  Google Scholar 

  37. Sridevi, S., Nirmala, S.: ANFIS based decision support system for prenatal detection of Truncus Arteriosus congenital heart defect. Appl. Soft Comput. 46, 577–587 (2016). https://doi.org/10.1016/j.asoc.2015.09.002

    Article  Google Scholar 

  38. Tiwari, P., Colborn, K.L., Smith, D.E., Xing, F., Ghosh, D., Rosenberg, M.A.: Assessment of a machine learning model applied to harmonized electronic health record data for the prediction of incident atrial fibrillation. JAMA Netw. Open 3(1), e1919396 (2020). https://doi.org/10.1001/jamanetworkopen.2019.19396

    Article  Google Scholar 

  39. Tse, G., et al.: Multi-modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction \(\le ~\)45%. ESC Heart Fail. 7(6), 3716–3725 (2020). https://doi.org/10.1002/ehf2.12929

    Article  Google Scholar 

  40. Volta Medical lève 23 millions d’euros pour son IA en cardiologie, January 2021

    Google Scholar 

  41. Walli-Attaei, M., et al.: Variations between women and men in risk factors, treatments, cardiovascular disease incidence, and death in 27 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. The Lancet 396(10244), 97–109 (2020). https://doi.org/10.1016/S0140-6736(20)30543-2

    Article  Google Scholar 

  42. Wang, Q., et al.: Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure. ESC Heart Fail. 8(6), 5363–5371 (2021). https://doi.org/10.1002/ehf2.13627

    Article  Google Scholar 

  43. Woodward, M., Brindle, P., Tunstall-Pedoe, H., SIGN Group on Risk Estimation: Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart (Br. Card. Soc.) 93(2), 172–176 (2007). https://doi.org/10.1136/hrt.2006.108167

  44. Yang, Y., Yang, J., Feng, J., Wang, Y.: Early diagnosis of acute ischemic stroke by brain computed tomography perfusion imaging combined with head and neck computed tomography angiography on deep learning algorithm. Contrast Media Mol. Imaging 2022, 5373585 (2022). https://doi.org/10.1155/2022/5373585

    Article  Google Scholar 

  45. Yin, M., et al.: Influence of optimization design based on artificial intelligence and Internet of Things on the electrocardiogram monitoring system. J. Healthc. Eng. 2020, 8840910 (2020). https://doi.org/10.1155/2020/8840910

    Article  Google Scholar 

  46. Yuan, H., et al.: Development of heart failure risk prediction models based on a multi-marker approach using random forest algorithms. Chin. Med. J. 132(7), 819–826 (2019). https://doi.org/10.1097/CM9.0000000000000149

    Article  Google Scholar 

  47. Yusuf, S., Reddy, S., Ôunpuu, S., Anand, S.: Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation 104(22), 2746–2753 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatou Lo Niang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Niang, F.L., Houndji, V.R., Lô, M., Degila, J., Ba, M.L. (2023). Use of Artificial Intelligence in Cardiology: Where Are We in Africa?. In: Saeed, R.A., Bakari, A.D., Sheikh, Y.H. (eds) Towards new e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 499. Springer, Cham. https://doi.org/10.1007/978-3-031-34896-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34896-9_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34895-2

  • Online ISBN: 978-3-031-34896-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics