TY - JOUR
T1 - COVID-19 pathways for brain and heart injury in comorbidity patients
T2 - A role of medical imaging and artificial intelligence-based COVID severity classification: A review
AU - Suri, Jasjit S.
AU - Puvvula, Anudeep
AU - Biswas, Mainak
AU - Majhail, Misha
AU - Saba, Luca
AU - Faa, Gavino
AU - Singh, Inder M.
AU - Oberleitner, Ronald
AU - Turk, Monika
AU - Chadha, Paramjit S.
AU - Johri, Amer M.
AU - Sanches, J. Miguel
AU - Khanna, Narendra N.
AU - Viskovic, Klaudija
AU - Mavrogeni, Sophie
AU - Laird, John R.
AU - Pareek, Gyan
AU - Miner, Martin
AU - Sobel, David W.
AU - Balestrieri, Antonella
AU - Sfikakis, Petros P.
AU - Tsoulfas, George
AU - Protogerou, Athanasios
AU - Misra, Durga Prasanna
AU - Agarwal, Vikas
AU - Kitas, George D.
AU - Ahluwalia, Puneet
AU - Kolluri, Raghu
AU - Teji, Jagjit
AU - Maini, Mustafa Al
AU - Agbakoba, Ann
AU - Dhanjil, Surinder K.
AU - Sockalingam, Meyypan
AU - Saxena, Ajit
AU - Nicolaides, Andrew
AU - Sharma, Aditya
AU - Rathore, Vijay
AU - Ajuluchukwu, Janet N.A.
AU - Fatemi, Mostafa
AU - Alizad, Azra
AU - Viswanathan, Vijay
AU - Krishnan, Pudukode R.
AU - Naidu, Subbaram
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/9
Y1 - 2020/9
N2 - Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients—specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.
AB - Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients—specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.
KW - Artificial intelligence
KW - Brain
KW - COVID-19
KW - Comorbidity
KW - Heart
KW - Imaging
KW - Lung
KW - Pathophysiology
KW - Risk assessment
UR - http://www.scopus.com/inward/record.url?scp=85090163898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090163898&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2020.103960
DO - 10.1016/j.compbiomed.2020.103960
M3 - Review article
C2 - 32919186
AN - SCOPUS:85090163898
SN - 0010-4825
VL - 124
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 103960
ER -