Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

References

• Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial Intelligence in Healthcare. Academic Press; 2020:25–60.
   Google Scholar
• Peters DH, Garg A, Bloom G, Walker DG, Brieger WR, Hafizur Rahman M. Poverty and access to health care in developing countries. Ann N Y Acad Sci. 2008;1136(1):161–171. doi:10.1196/annals.1425.011
   PubMed Web of Science ®Google Scholar
• Hasan BS, Rasheed MA, Wahid A, Kumar RK, Zuhlke L. Generating evidence from contextual clinical research in low- to middle income countries: a roadmap based on theory of change. Front Pediatr. 2021;9:764239. doi:10.3389/fped.2021.764239
   PubMed Web of Science ®Google Scholar
• Anand S, Bradshaw C, Prabhakaran D. Prevention and management of CVD in LMICs: why do ethnicity, culture, and context matter? BMC Med. 2020;18(1):7. doi:10.1186/s12916-019-1480-9
   PubMed Web of Science ®Google Scholar
• Reichert HA, Rath TE. Cardiac surgery in developing countries. J Extra Corpor Technol. 2017;49(2):98.
   PubMedGoogle Scholar
• Saxena A. Status of pediatric cardiac care in developing countries. Children. 2019;6(2):34. doi:10.3390/children6020034
   PubMedGoogle Scholar
• Dictionary.apa.org. n.d. APA dictionary of psychology. Available from: https://dictionary.apa.org/learning. Accessed December 13, 2020.
   Google Scholar
• Mathur P, Srivastava S, Xu X, Mehta JL. Artificial Intelligence, Machine Learning, and Cardiovascular Disease. Clin Med Insights Cardiol. 2020;14:1179546820927404. doi:10.1177/1179546820927404
   Web of Science ®Google Scholar
• Hoodbhoy Z, Jiwani U, Sattar S, Salam R, Hasan B, Das JK. Diagnostic accuracy of machine learning models to identify congenital heart disease: a meta-analysis. Front Artificial Intelligence. 2021;8(4):97.
   Google Scholar
• Amisha PM, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Primary Care. 2019;8(7):2328. doi:10.4103/jfmpc.jfmpc_440_19
   PubMedGoogle Scholar
• Behzadi-khormouji H, Rostami H, Salehi S, et al. Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images. Comput Methods Programs Biomed. 2020;185:105162. doi:10.1016/j.cmpb.2019.105162
   PubMed Web of Science ®Google Scholar
• Yuan Q, Zhang H, Deng T, et al. Role of Artificial Intelligence in Kidney Disease. Int J Med Sci. 2020;17(7):970. doi:10.7150/ijms.42078
   PubMed Web of Science ®Google Scholar
• Patel UK, Anwar A, Saleem S, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. 2019;26:1–20.
   Google Scholar
• Gomolin A, Netchiporouk E, Gniadecki R, Litvinov IV, Liangpunsakul S. Artificial Intelligence Applications in Dermatology: where Do We Stand? Front Med. 2020;7:7. doi:10.3389/fmed.2020.00007
   Web of Science ®Google Scholar
• Gubbi S, Hamet P, Tremblay J, Koch CA, Hannah-Shmouni F. Artificial intelligence and machine learning in endocrinology and metabolism: the Dawn of a new era. Front Endocrinol (Lausanne). 2019;28(10):185. doi:10.3389/fendo.2019.00185
   Google Scholar
• Jarvis T, Thornburg D, Rebecca AM, Teven CM. Artificial Intelligence in Plastic Surgery: current Applications, Future Directions, and Ethical Implications. Plastic Reconstructive Surgery Global Open. 2020;8(10):e3200. doi:10.1097/GOX.0000000000003200
   PubMedGoogle Scholar
• Iftikhar P, Kuijpers MV, Khayyat A, Iftikhar A, De Sa MD. Artificial Intelligence: a New Paradigm in Obstetrics and Gynecology Research and Clinical Practice. Cureus. 2020;12(2):548.
   Web of Science ®Google Scholar
• Dananjayan S, Raj GM. Artificial Intelligence during a pandemic: the COVID‐19 example. Int J Health Plann Manage. 2020;1:20.
   Google Scholar
• Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;7:369.
   Google Scholar
• Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digital Med. 2020;3(1):1–8. doi:10.1038/s41746-020-00324-0
   PubMed Web of Science ®Google Scholar
• Fernholz T The World Bank is eliminating the term “developing country” from its data vocabulary [Internet]. Quartz; 2016. Available from: https://qz.com/685626/the-world-bank-is-eliminating-The-term-developing-country-from-its-data-vocabulary/. Accessed January 17, 2023.
   Google Scholar
• Vasconcellos AG. Revisiting the Concept of Innovative Developing Countries (Idcs) for Its Relevance to Health Innovation and Neglected Tropical Diseases and for the Prevention and Control of Epidemics. PLoS Neglected Trop Dis. 2018;12(7):e0006469.
   PubMed Web of Science ®Google Scholar
• Owolabi M, Miranda JJ, Yaria J, Ovbiagele B. Controlling cardiovascular diseases in low and middle income countries by placing proof in pragmatism. BMJ Global Health. 2016;1(3):e000105. doi:10.1136/bmjgh-2016-000105
   PubMed Web of Science ®Google Scholar
• Bovet P, Paccaud F. Cardiovascular disease and the changing face of global public health: a focus on low and middle income countries. Public Health Rev. 2011;33(2):397–415. doi:10.1007/BF03391643
   Google Scholar
• Bloomfield GS, Peña MS. Five Reasons Why Global Health Matters to Cardiologists. Cardiol Clin. 2017;35(1):xiii–v. doi:10.1016/j.ccl.2016.10.001
   PubMed Web of Science ®Google Scholar
• Rosengren A, Smyth A, Rangarajan S, et al. Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: the Prospective Urban Rural Epidemiologic (PURE) study. Lancet Global Health. 2019;7(6):e748–60. doi:10.1016/S2214-109X(19)30045-2
   PubMed Web of Science ®Google Scholar
• World Health Statistics 2020 visual summary [Internet]. Who.int. Available from: https://www.who.int/data/gho/whs-2020-visual-summary. Accessed January 17, 2023.
   Google Scholar
• Cooper RA. States With More Physicians Have Better-Quality Health Care: at the state level, increased numbers of both family physicians and specialists per capita are associated with higher quality of health care. Health Aff. 2008;27(Suppl1):w91–102. doi:10.1377/hlthaff.28.1.w91
   Google Scholar
• Chow CK, Nguyen TN, Marschner S, et al. Availability and affordability of medicines and cardiovascular outcomes in 21 high-income, middle-income and low-income countries. BMJ Global Health. 2020;5(11):e002640. doi:10.1136/bmjgh-2020-002640
   PubMed Web of Science ®Google Scholar
• Celermajer DS, Chow CK, Marijon E, Anstey NM, Woo KS. Cardiovascular disease in the developing world: prevalences, patterns, and the potential of early disease detection. J Am Coll Cardiol. 2012;60(14):1207–1216. doi:10.1016/j.jacc.2012.03.074
   PubMedGoogle Scholar
• Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394(10201):861–867. doi:10.1016/S0140-6736(19)31721-0
   PubMed Web of Science ®Google Scholar
• Bhalla V, Isakson S, Bhalla MA, et al. Diagnostic ability of B-type natriuretic peptide and impedance cardiography: testing to identify left ventricular dysfunction in hypertensive patients. Am J Hypertens. 2005;18(S2):73S–81S. doi:10.1016/j.amjhyper.2004.11.044
   PubMedGoogle Scholar
• Wu JT, Wang SL, Chu YJ, et al. CHADS2 and CHA2DS2-VASc scores predict the risk of ischemic stroke outcome in patients with interatrial block without atrial fibrillation. J Atheroscler Thromb. 2016;1:34900.
   Google Scholar
• Increase Productivity | control Health Care Costs | model | workplace Health Promotion | CDC [Internet]. Available from: https://www.cdc.gov/workplacehealthpromotion/model/control-costs/benefits/productivity.html. Accessed January 17, 2023.
   Google Scholar
• Gevaert AB, Tibebu S, Mamas MA, et al. Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes. ESC Heart Failure. 2021;8(4):2741–2754. doi:10.1002/ehf2.13344
   PubMed Web of Science ®Google Scholar
• Kobayashi M, Huttin O, Magnusson M, et al. Machine learning-derived echocardiographic phenotypes predict heart failure incidence in asymptomatic individuals. Cardiovascular Imaging. 2022;15(2):193–208.
   Google Scholar
• Kim M, Kang Y, You SC, et al. Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices. Sci Rep. 2022;12(1):10. doi:10.1038/s41598-021-04021-0
   PubMed Web of Science ®Google Scholar
• Sampedro-Gómez J, Dorado-Díaz PI, Vicente-Palacios V, et al. Machine learning to predict stent restenosis based on daily demographic, clinical, and angiographic characteristics. Canadian J Cardiol. 2020;36(10):1624–1632. doi:10.1016/j.cjca.2020.01.027
   PubMed Web of Science ®Google Scholar
• Min HS, Ryu D, Kang SJ, et al. Prediction of coronary stent underexpansion by pre-procedural intravascular ultrasound–based deep learning. Cardiovascular Interventions. 2021;14(9):1021–1029. doi:10.1016/j.jcin.2021.01.033
   Google Scholar
• Cho H, Kang SJ, Min HS, et al. Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease. Atherosclerosis. 2021;1(324):69–75. doi:10.1016/j.atherosclerosis.2021.03.037
   Google Scholar
• Eisenberg E, McElhinney PA, Commandeur F, et al. Deep learning–based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects. Circ Cardiovasc Imaging. 2020;13(2):e009829. doi:10.1161/CIRCIMAGING.119.009829
   PubMed Web of Science ®Google Scholar
• Fernández-Ruiz I. Artificial intelligence to improve the diagnosis of cardiovascular diseases. Nat Rev Cardiol. 2019;16(3):133. doi:10.1038/s41569-019-0158-5
   PubMed Web of Science ®Google Scholar
• Miller RJ, Huang C, Liang JX, Slomka PJ. Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology. J Nuclear Cardiol. 2022;4:1–9.
   Google Scholar
• Christopoulos G, Graff-Radford J, Lopez CL, et al. Artificial intelligence–electrocardiography to predict incident atrial fibrillation: a population-based study. Circ Arrhythm Electrophysiol. 2020;13(12):e009355. doi:10.1161/CIRCEP.120.009355
   PubMed Web of Science ®Google Scholar
• Siontis KC, Friedman PA. The Role of Artificial Intelligence in Arrhythmia Monitoring. Card Electrophysiol Clin. 2021;13(3):543–554. doi:10.1016/j.ccep.2021.04.011
   PubMedGoogle Scholar
• Tsay D, Patterson C. From machine learning to artificial intelligence applications in cardiac care: real-world examples in improving imaging and patient access. Circulation. 2018;138(22):2569–2575. doi:10.1161/CIRCULATIONAHA.118.031734
   PubMed Web of Science ®Google Scholar
• Vashistha R, Dangi AK, Kumar A, Chhabra D, Shukla P. Futuristic biosensors for cardiac health care: an artificial intelligence approach. Biotech. 2018;8(8):1. doi:10.1007/s13205-018-1368-y
   Google Scholar
• Massalha S, Clarkin O, Thornhill R, Wells G, Chow BJ. Decision support tools, systems, and artificial intelligence in cardiac imaging. Canadian J Cardiol. 2018;34(7):827–838. doi:10.1016/j.cjca.2018.04.032
   PubMed Web of Science ®Google Scholar
• Schwalm JD, Di S, Sheth T, et al. A machine learning–based clinical decision support algorithm for reducing unnecessary coronary angiograms. Cardiovascular Digital Health j. 2022;3(1):21–30. doi:10.1016/j.cvdhj.2021.12.001
   PubMedGoogle Scholar
• Sardar P, Abbott JD, Kundu A, Aronow HD, Granada JF, Giri J. Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance. JACC Cardiovasc Interv. 2019;12(14):1293–1303. doi:10.1016/j.jcin.2019.04.048
   PubMed Web of Science ®Google Scholar
• Sim I. Mobile devices and health. N Eng J Med. 2019;381(10):956–968. doi:10.1056/NEJMra1806949
   PubMed Web of Science ®Google Scholar
• Holzer R, Ladusans E, Kitchiner D, Peart I, Gladman G, Miles G. Prioritization of congenital cardiac surgical patients using fuzzy reasoning-a solution to the problem of the waiting list? Cardiol Young. 2006;16(3):289. doi:10.1017/S1047951106000400
   PubMed Web of Science ®Google Scholar
• Thomford NE, Bope CD, Agamah FE, et al. Implementing artificial intelligence and digital health in resource-limited settings? Top 10 lessons we learned in congenital heart defects and cardiology. j Integrative Biol. 2020;24(5):264–277. doi:10.1089/omi.2019.0142
   Web of Science ®Google Scholar
• Quer G, Arnaout R, Henne M, Arnaout R. Machine Learning and the Future of Cardiovascular Care: JACC State-of-The-Art Review. J Am Coll Cardiol. 2021;77(3):300–313. doi:10.1016/j.jacc.2020.11.030
   PubMed Web of Science ®Google Scholar
• Kshetri N. Artificial Intelligence in Developing Countries. IEEE Ann Hist Comput. 2020;22(4):63–68.
   Google Scholar
• Pena MS, Bloomfield GS. Cardiovascular disease research and the development agenda in low-and middle-income countries. Glob Heart. 2015;10(1):71. doi:10.1016/j.gheart.2014.12.006
   PubMedGoogle Scholar
• Evashwick C. Creating the continuum of care. Health Matrix. 1989;7(1):30–39.
   PubMedGoogle Scholar
• National Cancer Institute. NCI Dictionary of Cancer Terms; 2022. Available from: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/continuum-of-care. Accessed January 12, 2022.
   Google Scholar
• Nair SM, Zheleva B, Dobrzycka A, Hesslein P, Sadanandan R, Kumar RK. A Population Health Approach to Address the Burden of Congenital Heart Disease in Kerala, India. Glob Heart. 2021;16(1). doi:10.5334/gh.1034
   PubMed Web of Science ®Google Scholar
• Bhavnani SP, Sola S, Adams D, Venkateshvaran A, Dash PK, Sengupta PP. A randomized trial of pocket-echocardiography integrated mobile health device assessments in modern structural heart disease clinics. JACC Cardiovasc Imaging. 2018;11(4):546–557. doi:10.1016/j.jcmg.2017.06.019
   PubMed Web of Science ®Google Scholar
• Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;1:1–4.
   Google Scholar
• FDA Authorizes Marketing of First Cardiac Ultrasound Software That Uses Artificial Intelligence to Guide User [Internet]. U.S. Food and Drug Administration. 2020 Available from: https://www.fda.gov/news-events/press-announcements/fda-authorizes-marketing-first-cardiac-ultrasound-software-uses-artificial-intelligence-guide-user. Accessed January 17, 2023.
   Google Scholar
• Narang A, Bae R, Hong H, et al. Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. JAMA cardiol. 2021;6(6):624. doi:10.1001/jamacardio.2021.0185
   PubMed Web of Science ®Google Scholar
• November 2018 510(k) Clearances [Internet]. U.S. Food and Drug Administration; 2018 Available from: https://www.fda.gov/medical-devices/510k-clearances/november-2018-510k-clearances. Accessed January 17, 2023.
   Google Scholar
• AI ECG-products-carewell [Internet]. Available from: https://www.carewellhealth.com/products_aiecg.html. Accessed January 17, 2023.
   Google Scholar
• The Medical Futurist [Internet]. The Medical Futurist Available from: https://medicalfuturist.com/fda-approved-ai-based-algorithms/. Accessed January 17, 2023.
   Google Scholar
• Lewis T, Synowiec C, Lagomarsino G, Schweitzer J. E-health in low-and middle-income countries: findings from the Center for Health Market Innovations. Bull World Health Organ. 2012;90:332–340. doi:10.2471/BLT.11.099820
   PubMed Web of Science ®Google Scholar
• Babigumira JB, Jenny AM, Bartlein R, Stergachis A, Garrison LP. Health technology assessment in low-and middle-income countries: a landscape assessment. J Pharmaceutical Health Services Res. 2016;7(1):37–42. doi:10.1111/jphs.12120
   Google Scholar
• Fenech ME, Buston O. AI in cardiac imaging: a UK-based perspective on addressing the ethical, social, and political challenges. Front Cardiovascular Med. 2020;15(7):54. doi:10.3389/fcvm.2020.00054
   Google Scholar
• Gaziano TA, Bitton A, Anand S, Abrahams-Gessel S, Murphy A. Growing epidemic of coronary heart disease in low-and middle-income countries. Curr Probl Cardiol. 2010;35(2):72–115. doi:10.1016/j.cpcardiol.2009.10.002
   PubMed Web of Science ®Google Scholar