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Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Authors :
Jasjit S. Suri
Anudeep Puvvula
Misha Majhail
Mainak Biswas
Ankush D. Jamthikar
Luca Saba
Gavino Faa
Inder M. Singh
Ronald Oberleitner
Monika Turk
Saurabh Srivastava
Paramjit S. Chadha
Harman S. Suri
Amer M. Johri
Vijay Nambi
J Miguel Sanches
Narendra N. Khanna
Klaudija Viskovic
Sophie Mavrogeni
John R. Laird
Arindam Bit
Gyan Pareek
Martin Miner
Antonella Balestrieri
Petros P. Sfikakis
George Tsoulfas
Athanasios Protogerou
Durga Prasanna Misra
Vikas Agarwal
George D. Kitas
Raghu Kolluri
Jagjit Teji
Michele Porcu
Mustafa Al-Maini
Ann Agbakoba
Meyypan Sockalingam
Ajit Sexena
Andrew Nicolaides
Aditya Sharma
Vijay Rathore
Vijay Viswanathan
Subbaram Naidu
Deepak L. Bhatt
Source :
Reviews in Cardiovascular Medicine, Vol 21, Iss 4, Pp 541-560 (2020)
Publication Year :
2020
Publisher :
IMR Press, 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 AIbased platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Details

Language :
English
ISSN :
15306550
Volume :
21
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Reviews in Cardiovascular Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.38aa60518c7c4c6da9aaaecc03fb56ca
Document Type :
article
Full Text :
https://doi.org/10.31083/j.rcm.2020.04.236