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Review

Cardiac Diagnosis with Machine Learning: A Paradigm Shift in Cardiac Care

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Article: 2031816 | Received 23 Nov 2020, Accepted 18 Jan 2022, Published online: 26 Jan 2022

References

  • Ainapure, B. S., R. N. Pise, A. A. Wagh, and J. Tejnani. 2021. Prognosis of COVID- 19 Patients with machine learning techniques. Annals of the Romanian Society for Cell Biology 25 (6):20183–1770.
  • Al’Aref, S. J., K. Anchouche, G. Singh, P. J. Slomka, K. K. Kolli, A. Kumar, M. Pandey, G. Maliakal, A. R. van Rosendael, A. N. Beecy, et al. 2019. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European Heart Journal 40(24):1975–86. View at: Publisher Site | Google Scholar doi:10.1093/eurheartj/ehy404.
  • Alsharqi, M., W. J. Woodward, J. A. Mumith, D. C. Markham, R. Upton, and P. Leeson. 2018. Artificial intelligence and echocardiography. Echo Research and Practice 5:R115–R125. View at: Publisher Site | Google Scholar doi:10.1530/ERP-18-0056.
  • Altintas, Z., W. M. Fakanya, and I. E. Tothill. October 2014. Cardiovascular disease detection using bio-sensing techniques. Talanta 128(1):177–86. doi: 10.1016/j.talanta.2014.04.060.
  • Anderson, L. 2005. Candidate-based proteomics in the search for biomarkers of cardiovascular disease. Journal of Physiology 563:23. doi:10.1113/jphysiol.2004.080473.
  • Arakaki, S., T. Hideshima, D. Nakagawa, T. Niwa, T. Tanaka, T. Matsunaga, and T. Osaka. 2004. Detection of biomolecular interaction between biotin and streptavidin on a self-assembled monolayer using magnetic nanoparticles. Biotechnology and Bioengineering 88:543–46. doi:10.1002/bit.20262.
  • Avendi, M. R., A. Kheradvar, and H. Jafarkhani. 2017. Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach. Magnetic Resonance in Medicine 78 (6):2439–48. View at: Publisher Site | Google Scholar. doi:10.1002/mrm.26631.
  • Aydin, S., K. Ugur, S. Aydin, İ. Sahin, and M. Yardim. 2019. Biomarkers in acute myocardial infarction: Current perspectives. Vascular Health and Risk Management 15:1. doi:10.2147/VHRM.S166157.
  • Azar, K. M. J., L. I. Lesser, B. Y. Laing, J. Stephens, M. S. Aurora, L. E. Burke, and L. P. Palaniappan . 2013. Mobile applications for weight management. American Journal of Preventive Medicine 45(5):583–89. doi:10.1016/j.amepre.2013.07.005.
  • Baessler, B., M. Mannil, S. Oebel, D. Maintz, H. Alkadhi, and R. Manka. 2018. Subacute and chronic left ventricular myocardial scar: Accuracy of texture analysis on nonenhanced cine MR images. Radiology 286 (1):103–12. View at: Publisher Site | Google Scholar. doi:10.1148/radiol.2017170213.
  • Barrett, M., J. Boyne, J. Brandts, H. P. Brunner-La Rocca, L. De Maesschalck, K. De Wit, B. Zippel-Schultz, C. Eurlings, D. Fitzsimons, and O. Golubnitschaja. 2019. Artificial intelligence supported patient self-care in chronic heart failure: A paradigm shift from reactive to predictive, preventive and personalised care. Epma Journal 10 (4):445–64. doi:10.1007/s13167-019-00188-9.
  • Bharti, R., A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh. 2021. Prediction of heart disease using a combination of machine learning and deep learning. Computational Intelligence and Neuroscience. 11. Article ID 8387680 doi:10.1155/2021/8387680.
  • Bohr, A., and K. Memarzadeh. 2020. The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare, 25–60. Academic Press. doi: 10.1016/B978-0-12-818438-7.00002-2.
  • Burke, L. E., J. Ma, K. M. J. Azar, G. G. Bennett, E. D. Peterson, Y. Zheng, W. Riley, J. Stephens, S. H. Shah, B. Suffoletto, et al. 2015. Current science on consumer use of mobile health for cardiovascular disease prevention. Circulation 132(12):1157–213. View at: Publisher Site | Google Scholar doi:10.1161/CIR.0000000000000232.
  • Cheema, B. S., C. Hsieh, D. Adams, A. Narang, and J. Thomas. 2019. Automated guidance and image capture of echocardiographic views using a deep learning-derived technology. Circulation 140: ([poster]). pp. A15694-A15694.
  • Cho, I. H., E. H. Paek, Y. K. Kim, J. H. Kim, and S. H. Paek. 2009. Chemiluminometric enzymelinked immunosorbent assays (ELISA)-on-a-chip biosensor based on cross-flow chromatography. Analytica Chimica Acta 632:247–55. doi:10.1016/j.aca.2008.11.019.
  • Cikes, M., S. Sanchez-Martinez, B. Claggett, N. Duchateau, G. Piella, C. Butakoff, A. C. Pouleur, D. Knappe, T. Biering-Sørensen, V. Kutyifa, et al. 2019. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. European Journal of Heart Failure 21(1):74–85. View at: Publisher Site | Google Scholar doi:10.1002/ejhf.1333.
  • Daniel Duprez, F. E. S. C. January 2006. Early detection of cardiovascular disease - the future of cardiology? E-Journal of Cardiology Practice 4:19–26.
  • Daniels, J. S., and N. Pourmand. 2007. Label-free impedance biosensors: Opportunities and challenges. Electroanalysis 19:1239–57. doi:10.1002/elan.200603855.
  • Darain, F., P. Yager, K. L. Gan, and S. C. Tjin. 2009. On-chip detection of myoglobin based on fluorescence. Biosensors & Bioelectronics 24:1744–50. doi:10.1016/j.bios.2008.09.004.
  • Davis, A., K. Billick, K. Horton, M. Jankowski, J. E. Peg Knoll, A. P. Marshall, R. Palma, and D. B. Adams. 2018. Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: Multicentre validation study. European Heart Journal Cardiovascular Imaging 19:47–58.
  • Demirkiran, A., H. Everaars, R. P. Amier, C. Beijnink, M. J. Bom, M. J. W. Götte, R. B. van Loon, J. L. Selder, A. C. van Rossum, R. Nijveldt, et al. 2019. Cardiovascular magnetic resonance techniques for tissue characterisation after acute myocardial injury. European Heart Journal Cardiovascular Imaging 20(7):723–34. 10.1093/ehjci/jez094 [PubMed] [CrossRef] [Google Scholar] F1000 Recommendation doi: 10.1093/ehjci/jez094.
  • Dhingra, R., and R. S. Vasan. 2017. Biomarkers in cardiovascular disease: Statistical assessment and section on key novel heart failure biomarkers. Trends in Cardiovascular Medicine 27 (2):123–33. doi:10.1016/j.tcm.2016.07.005.
  • Fan, X., I. M. White, S. I. Shopova, H. Zhu, J. D. Suter, and Y. Sun. 2008. Sensitive optical biosensors for unlabeled targets: A review. Analytica Chimica Acta 620:8–26. doi:10.1016/j.aca.2008.05.022.
  • GBD 2017 Causes of Death Collaborators. 2018. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: A systematic analysis for the global burden of disease study. Lancet 392:1736–88.
  • Ghantous, C. M., L. Kamareddine, R. Farhat, F. A. Zouein, S. Mondello, F. Kobeissy, and A. Zeidan. 2020. Advances in cardiovascular biomarker discovery. Biomedicines 8 (12):552.
  • Haleem, A., M. Javaid, R. P. Singh, and R. Suman. 2021. Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. Sustainable Operations and Computers 2:71–78. doi:10.1016/j.susoc.2021.04.003.
  • Han, J., and M. Kamber. 2011. Data Mining: Concepts and Techniques. 3rd ed. Burlington: Morgan Kaufmann.
  • Homola, J, Piliarik, M. 2006. Surface Plasmon Resonance (SPR) Sensors. In: Homola J. (eds) Surface Plasmon Resonance Based Sensors. Springer Series on Chemical Sensors and Biosensors, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/5346_014
  • Honikel, M. M., C. E. Lin, D. Probst, and J. T. La Belle. 2018. Facilitating earlier diagnosis of cardiovascular disease through point-of-care biosensors: A review. Critical Reviews in Biomedical Engineering 46 (1):53–82. doi:10.1615/CritRevBiomedEng.2018025818.
  • Horiuchi, Y., S. Tanimoto, A. H. M. M. Latif, K. Y. Urayama, J. Aoki, K. Yahagi, T. Okuno, Y. Sato, T. Tanaka, K. Koseki, et al. 2018. Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables. International Journal of Cardiology 262:57–63. View at: Publisher Site | Google Scholar doi:10.1016/j.ijcard.2018.03.098.
  • Introduction to machine learning with python. A. C. Müller, and S. Guido, Sebastopol, CA, USA: O’Reilly Media, Inc. October 2016.
  • Jagannathan, R., S. A. Patel, M. K. Ali, and K. V. Narayan. 2019. Global updates on cardiovascular disease mortality trends and attribution of traditional risk factors. Current Diabetes Reports 19 (7):1–12. doi:10.1007/s11892-019-1161-2.
  • Jiang, F., Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, and Y. Wang. 2017. Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology 2:4. doi:10.1136/svn-2017-000101.
  • Joachims, T. 1998. Making large-scale SVM learning practical. Adv. Kernel Methods - Support Vector Learn, MIT Press.
  • Johnson, K. W., S. J. Torres, B. S. Glicksberg, K. Shameer, R. Miotto, M. Ali, E. Ashley, and J. T. Dudley . 2018. Artificial intelligence in cardiology. Journal of the American College of Cardiology 71::2668–79. doi:10.1016/j.jacc.2018.03.521.
  • Joseph, P., D. Leong, M. McKee, S. S. Anand, J. D. Schwalm, K. Teo, S. Yusuf, and S. Yusuf. 2017. Reducing the global burden of cardiovascular disease, part 1: The epidemiology and risk factors. Circulation Research 121 (6):677–94. doi:10.1161/CIRCRESAHA.117.308903.
  • Keates, A. K., A. O. Mocumbi, M. Ntsekhe, K. Sliwa, and S. Stewart. 2017. Cardiovascular disease in Africa: Epidemiological profile and challenges. Nature Reviews. Cardiology 14 (5):273–93. doi:10.1038/nrcardio.2017.19.
  • Krishnamoorthy, S., A. A. Iliadis, T. Bei, and G. P. Chrousos. 2008. An interleukin-6 ZnO/SiO(2)/Si surface acoustic wave biosensor. Biosensors & Bioelectronics 24:313–18. doi:10.1016/j.bios.2008.04.011.
  • Krittanawong, C., A. J. Rogers, K. W. Johnson, Z. Wang, M. P. Turakhia, J. L. Halperin, and S. M. Narayan. 2021. Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nature Reviews. Cardiology 18 (2):75–91. doi:10.1038/s41569-020-00445-9.
  • Krittanawong, C., H. U. H. Virk, S. Bangalore, Z. Wang, K. W. Johnson, R. Pinotti, H. Zhang, S. Kaplin, B. Narasimhan, T. Kitai, et al. 2020. Machine learning prediction in cardiovascular diseases: A meta-analysis. Scientific Reports 10:16057. doi:10.1038/s41598-020-72685-1.
  • Kulkarni, A., and S. Vijaykumar. 2016. Application of internet of things in artificial heart pacemakers and its impact on security. International Journal of Current Trends in Engineering & Research (IJCTER) 2 (5):604–10. View at: Google Scholar.
  • Kunduru, V., M. Bothara, J. Grosch, S. Sengupta, P. K. Patra, and S. Prasad. 2010. Nanostructured surfaces for enhanced protein detection toward clinical diagnostics. Nanomedicine: Nanotechnology, Biology and Medicine 6 (5):642–50. doi:10.1016/j.nano.2010.03.002.
  • Kwan, G. F., B. M. Mayosi, A. O. Mocumbi, J. J. Miranda, M. Ezzati, Y. Jain, G. Robles, E. J. Benjamin, S. V. Subramanian, and G. Bukhman. 2016. Endemic cardiovascular diseases of the poorest billion. Circulation 133 (24):2561–75. PubMed] [Google Scholar. doi:10.1161/CIRCULATIONAHA.116.008731.
  • Kwon, Y. C., M. G. Kim, E. M. Kim, Y. B. Shin, S. K. Lee, S. D. Lee, M. J. Cho, and H. S. Ro. 2011. Development of a surface plasmon resonance-based immunosensor for the rapid detection of cardiac troponin I. Biotechnology Letters 33:921–27. doi:10.1007/s10529-010-0509-0.
  • Lancaster, M. C., A. M. Salem Omar, S. Narula, H. Kulkarni, J. Narula, and P. P. Sengupta. 2019. Phenotypic clustering of left ventricular diastolic function parameters: Patterns and prognostic relevance. JACC. Cardiovascular Imaging 12 (7):1149–61. View at: Publisher Site | Google Scholar. doi:10.1016/j.jcmg.2018.02.005.
  • Leung, W. M., C. P. Chan, M. F. Leung, R. Renneberg, K. Lehmann, I. Renneberg, M. Lehmann, A. Hempel, and J. F. C. Glatz. 2005. Novel digital-style rapid test simultaneously detecting heart attack and predicting cardiovascular disease risk. Analytical Letters 38:423–39. doi:10.1081/AL-200045139.
  • Levin, D. C., L. Parker, E. J. Halpern, and V. M. Rao. 2019. Coronary CT angiography: Reversal of earlier utilisation trends. Journal of the American College of Radiology: JACR 16:147–55. [PubMed] [CrossRef] [Google Scholar] doi:10.1016/j.jacr.2018.07.022.
  • McDonnell, B., S. Hearty, P. Leonard, and R. O’Kennedy. 2009. Cardiac biomarkers and the case for point-of-care testing. Clinical Biochemistry 42:549–61. doi:10.1016/j.clinbiochem.2009.01.019.
  • Mitchell, T. 1997. Machine Learning. New York: McGraw Hill.
  • Monson, C. F., L. N. Driscoll, E. Bennion, C. J. Miller, and M. Majda. 2009. Antibody–antigen exchange equilibria in a field of an external force: Design of reagentless biosensors. Analytical Chemistry 81:7510–14. doi:10.1021/ac9010759.
  • Murdoch, T. B., and A. S. Detsky. 2013. The inevitable application of big data to health care. Journal of the American Medical Association 309 (13):1351–52. View at: Publisher Site | Google Scholar. doi:10.1001/jama.2013.393.
  • Nan Wang, H., N. Liu, -Y.-Y. Zhang, D.-W. Feng, F. Huang, D.-S. Li, and Y.-M. Zhang . 2020. Reinforcement learning: A survey. Frontiers of Information Technology and Electronic Engineering 21(12):1726–44. doi:10.1631/FITEE.1900533.
  • Neve, M., P. J. Morgan, P. R. Jones, and C. E. Collins. 2010. Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: A systematic review with meta-analysis. Obesity Reviews 11 (4):306–21. View at: Publisher Site | Google Scholar. doi:10.1111/j.1467-789X.2009.00646.x.
  • Nikam, A., S. Bhandari, A. Mhaske, and S. Mantri. 2020. Cardiovascular disease prediction using machine learning models. IEEE Pune Section International Conference (PuneCon), Pune, India. doi:10.1109/PuneCon50868.2020.9362367.
  • Ohu, I., P. K. Benny, S. Rodrigues, and J. N. Carlson. 2020. Applications of machine learning in acute care research. Journal of the American College of Emergency Physicians Open 1 (5):766–72. doi:10.1002/emp2.12156.
  • Ouyang, D., B. He, A. Ghorbani, N. Yuan, J. Ebinger, C. P. Langlotz, P. A. Heidenreich, R. A. Harrington, D. H. Liang, E. A. Ashley, et al. 2020. Video-based AI for beat-to-beat assessment of cardiac function. Nature 580(7802):252–56. doi:10.1038/s41586-020-2145-8.
  • Park, J. P., D. M. Cropek, and S. Banta. 2010. High affinity peptides for the recognition of the heart disease biomarker troponin I identified using phage display. Biotechnology and Bioengineering 105:678–86. doi:10.1002/bit.22597.
  • Patel, J., and R. Goyal. 2008. Applications of artificial neural networks in medical science. Current Clinical Pharmacology 2 (3):217–26. doi:10.2174/157488407781668811.
  • Perez, M. V., K. W. Mahaffey, H. Hedlin, J. S. Rumsfeld, A. Garcia, T. Ferris, V. Balasubramanian, A. M. Russo, A. Rajmane, L. Cheung, et al. 2019. Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine 381(20):1909–17. View at: Publisher Site | Google Scholar doi:10.1056/NEJMoa1901183.
  • Perl, L., E. Soifer, J. Bartunek, D. Erdheim, F. Köhler, W. T. Abraham, and D. Meerkin . 2019. A novel wireless left atrial pressure monitoring system for patients with heart failure, first ex-vivo and animal experience. Journal of Cardiovascular Translational Research 12(4):290–98. doi:10.1007/s12265-018-9856-3.
  • Reinstadler, S. J., H. Thiele, and I. Eitel. 2015. Risk stratification by cardiac magnetic resonance imaging after ST-elevation myocardial infarction. Current Opinion in Cardiology 30 (6):681–89. 10.1097/HCO.0000000000000227 [PubMed] [CrossRef] [Google Scholar].
  • Rivera, J., A. McPherson, J. Hamilton, C. Birken, M. Coons, S. Iyer, A. Agarwal, C. Lalloo, and J. Stinson . 2016. Mobile apps for weight management: A scoping review. JMIR mHealth and uHealth 4(3):e87. doi:10.2196/mhealth.5115.
  • Romiti, S., M. Vinciguerra, W. Saade, I. A. Cortajarena, and E. Greco. 2020. Artificial intelligence (AI) and cardiovascular diseases: An unexpected Alliance. Cardiology Research and Practice 8. Article ID 4972346 doi:10.1155/2020/4972346.
  • Sarker, I. H. 2021. Machine learning: algorithms, real-world applications and research directions. SN Computer Science 2 (160). doi: 10.1007/s42979-021-00592-x.
  • Schernthaner, G., N. Shehadeh, A. S. Ametov, A. V. Bazarova, F. Ebrahimi, P. Fasching, Ž. Visockienė, P. Kempler, I. Konrāde, and N. M. Lalić. 2020. Worldwide inertia to the use of cardiorenal protective glucose-lowering drugs (SGLT2i and GLP-1 RA) in high-risk patients with type 2 diabetes. Cardiovascular Diabetology 19 (1):1–17. doi:10.1186/s12933-020-01154-w.
  • Seetharam, K., N. Kagiyama, and P. P. Sengupta. 2019. Application of mobile health, telemedicine and artificial intelligence to echocardiography. Echo Research and Practice 6 (2):R41–R52. doi:10.1530/ERP-18-0081.
  • Siontis, K. C., X. Yao, J. P. Pirruccello, A. A. Philippakis, and P. A. Noseworthy. 2020. How will machine learning inform the clinical care of atrial fibrillation? Circulation Research 127 (1):155–69. doi:10.1161/CIRCRESAHA.120.316401.
  • Stehlik, J., C. Schmalfuss, B. Bozkurt, Nativi-Nicolau, J., Wohlfahrt, P., Wegerich, S., Rose, K., Ray, R., Schofield, R., Deswal, A., et al. 2020. Continuous wearable monitoring analytics predict heart failure hospitalisation: The LINK-HF multicenter study. Circulation. Heart Failure 13(3): e006513.
  • Taiwo Oladipupo, Ayodele. 2010. Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (Ed.), ISBN: 978-953-307-034-6, InTech. http://www.intechopen.com/books/new-advances-in-machine-learning/types-of-machine-learning-algorithms
  • Tan, L. K., R. A. McLaughlin, E. Lim, Y. F. Abdul Aziz, and Y. M. Liew. 2018. Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression. Journal of Magnetic Resonance Imaging 48 (1):140–52. View at: Publisher Site | Google Scholar. doi:10.1002/jmri.25932.
  • Tweedie, M., R. Subramanian, P. Lemoine, I. Craig, E. T. McAdams, J. A. McLaughlin, B. Maccraith, and N. Kent. 2006. Fabrication of impedimetric sensors for label-free point-of-care immunoassay cardiac marker systems, with passive microfluidic delivery. Conference Proceedings – IEEE Engineering in Medicine and Biology Society, 1 4610–14.
  • Vasan, R. 2006. Biomarkers of cardiovascular disease: Molecular basis and practical considerations. Circulation 113:2335. doi:10.1161/CIRCULATIONAHA.104.482570.
  • Vashistha, R., A. K. Dangi, A. Kumar, D. Chhabra, and P. Shukla. 2018. Futuristic biosensors for cardiac health care: An artificial intelligence approach. 3 Biotechnology 8 (8):1–11. doi:10.1007/s13205-018-1368-y.
  • Walsh, J. A., E. J. Topol, and S. R. Steinhubl. 2014. Novel wireless devices for cardiac monitoring. Circulation 130 (7):573–81. View at: Publisher Site | Google Scholar. doi:10.1161/CIRCULATIONAHA.114.009024.
  • Winther, H. B., C. Hundt, B. Schmidt, C. Czerner, J. Bauersachs, F. Wacker, and J. Vogel-Claussen . 2018. ν-net: Deep Learning for Generalized Biventricular Mass and Function Parameters Using Multicenter Cardiac MRI Data. JACC. Cardiovascular Imaging 11(7):1036–38. doi:10.1016/j.jcmg.2017.11.013.
  • Yusuf, S., M. Pearson, H. Sterry, S. Parish, D. Ramsdale, P. Rossi, and P. Sleight. 1984. The entry ECG in the early diagnosis and prognostic stratification of patients with suspected acute myocardial infarction. European Heart Journal 5:690–96. doi:10.1093/oxfordjournals.eurheartj.a061728.
  • Zurada, J. M. 1992. Introduction to artificial neural systems. West, St. Paul, MN, USA