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COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

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Date
2020
Author
Suri, Jasjit S.
Puvvula, Anudeep
Biswas, Mainak
Majhail, Misha
Saba, Luca
Faa, Gavino
Singh, Inder M.
Oberleitner, Ronald
Turk, Monika
Chadha, Paramjit S.
Johri, Amer M.
Sanches, J. Miguel
Khanna, Narendra N.
Viskovic, Klaudija
Mavrogeni, Sophie
Laird, John R.
Pareek, Gyan
Miner, Martin
Sobel, David W.
Balestrieri, Antonella
Sfikakis, Petros P.
Tsoulfas, George
Protogerou, Athanasios
Prasanna Misra, Durga
Agarwal, Vikas
Kitas, George D.
Ahluwalia, Puneet
Kolluri, Raghu
Teji, Jagjit
Al Maini, Mustafa
Agbakoba, Ann
Dhanjil, Surinder K.
Sockalingam, Meyypan
Saxena, Ajit
Nicolaides, Andrew
Sharma, Aditya
Rathore, Vijay
Ajuluchukwu, Janet N.A.
Fatemi, Mostafa
Alizad, Azra
Viswanathan, Vijay
Krishnan, P.K.
Naidu, Subbaram
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Abstract
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. We conclude that imaging and AI-based tissue characterization, when considered alongside COVID-19 symptoms and their pre-test probabilities, offer a compelling solution for assessing the risk of comorbid patients. These methods show the potential to become an integral part of tracking and improving the healthcare system, both during the pandemic and beyond.
Palabras clave
COVID-19; Comorbidity; Pathophysiology; Heart; Brain; Lung; Imaging; Artificial intelligence; Risk assessment
URI
http://hdl.handle.net/20.500.12010/12543
Link to resource
https://doi.org/10.1016/j.compbiomed.2020.103960
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  • Documentos científicos relacionados a la COVID-19 [2292]
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Carrera 4 # 22-61 Teléfono: (+57 1) 242 7030 - 018000111022 Fax: (+57 1) 561 2107 Bogotá D.C., Colombia

Fundación Universitaria de Bogotá Jorge Tadeo Lozano | Vigilada Mineducación

Institución de educación superior privada, de utilidad común, sin ánimo de lucro y su carácter académico es el de Universidad.

Reconocimiento personería jurídica: Resolución 2613 del 14 de agosto de 1959 Minjusticia.

Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional.

 

Términos y condiciones | Políticas