IMR Press / RCM / Volume 21 / Issue 4 / DOI: 10.31083/j.rcm.2020.04.236
Open Access Review
Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence
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1 Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, 95747, CA, USA
2 Annu’s Hospitals for Skin and Diabetes, Nellore, 524001, AP, India
3 Oakmount High School and AtheroPoint™, Roseville, 95747, CA, USA
4 JIS University, Kolkata, 700001, West Bengal, India
5 Department of ECE, Visvesvaraya National Institute of Technology, Nagpur, 440010, MH, India
6 Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100, Cagliari, Italy
7 Department of Pathology, 09100, AOU of Cagliari, Italy
8 Behavior Imaging, Boise, 83701, ID, USA
9 The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27749, Delmenhorst, Germany
10 School of Computing Science & Engineering, Galgotias University, 201301, Gr. Noida, India
11 Brown University, Providence, 02912, RI, USA
12 Department of Medicine, Division of Cardiology, Queen’s University, Kingston, B0P 1R0, Ontario, Canada
13 Department of Cardiology, Baylor College of Medicine, 77001, TX, USA
14 Institute of Systems and Robotics, Instituto Superior Tecnico, 1000-001, Lisboa, Portugal
15 Department of Cardiology, Indraprastha APOLLO Hospitals, 110001, New Delhi, India
16 University Hospital for Infectious Diseases, 10000, Zagreb, Crotia
17 Cardiology Clinic, Onassis Cardiac Surgery Center, 104 31, Athens, Greece
18 Heart and Vascular Institute, Adventist Health St. Helena, St Helena, 94574, CA, USA
19 Department of Biomedical Engineering, NIT, Raipur, 783334, CG, India
20 Minimally Invasive Urology Institute, Brown University, Providence, 02901, Rhode Island, USA
21 Men’s Health Center, Miriam Hospital Providence, 02901, Rhode Island, USA
22 Rheumatology Unit, National Kapodistrian University of Athens, 104 31, Greece
23 Aristoteleion University of Thessaloniki, 544 53, Thessaloniki, Greece
24 National & Kapodistrian University of Athens, 104 31, Greece
25 Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, 226001, UP, India
26 Academic Affairs, Dudley Group NHS Foundation Trust, DY1, Dudley, UK
27 Arthritis Research UK Epidemiology Unit, Manchester University, M13, Manchester, UK
28 OhioHealth Heart and Vascular, 45874, Ohio, USA
29 Ann and Robert H. Lurie Children’s Hospital of Chicago, 60601, Chicago, USA
30 Allergy, Clinical Immunology and Rheumatology Institute, M3H 6A7, Toronto, Canada
31 University of Lagos, 100001, Lagos, Nigeria
32 MV Center of Diabetes, 600001, Chennai, India
33 Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, 999058, Cyprus
34 Division of Cardiovascular Medicine, University of Virginia, Charlottesville, 22901, VA, USA
35 Nephrology Department, Kaiser Permanente, Sacramento, 94203, CA, USA
36 MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, 600001, Chennai, India
37 Electrical Engineering Department, University of Minnesota, Duluth, 55801, MN, USA
38 Brigham and Women’s Hospital Heart & Vascular Center, Harvard Medical School, Boston, 02108, MA, USA
Rev. Cardiovasc. Med. 2020 , 21(4), 541–560; https://doi.org/10.31083/j.rcm.2020.04.236
Submitted: 2 November 2020 | Revised: 3 December 2020 | Accepted: 8 December 2020 | Published: 30 December 2020
Abstract

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic “cognitive” functions that we associate with our mind, such as “learning” and “solving problem”. New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Keywords
COVID-19
cardiovascular
myocarditis
artificial intelligence
risk assessment
non-invasive monitoring
Figures
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