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

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 1 st , 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.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“…Radiography provides many solutions for diagnosing COVID-19, like lung CT, chest X-rays, lung ultrasound, and PET/CT scans. The choice of modality depends on the patient’s comorbidities, age, and pre-test probabilities (PTPs) [ 18 ]. A PTP test on troponin levels (which is an indirect indicator of hypoxia) can be conducted to identify the severity of a COVID-19 infection.…”

“…AI is involved in many facets of COVID-19 management, such as medical imaging, risk assessment, telemedicine, and patient follow-up [ 16 ]. Even though AI has penetrated into several fields of radiological imaging [ 17 ], such as classification of images into (a) controls, (b) community viral pneumonia, and (c) COVID-19 pneumonia [ 18 ] [ 19 ] [ 20 ], the question that remains unanswered is whether these models actually function the way the pulmonologists and critical care physicians want. For example, if the patient has a specific pre-existing disease, can an AI include this information into its knowledgebase and use it to more effectively correlate this comorbid condition, the age factor, and the grayscale patterns in lung scans with COVID-19 severity?…”

Imaging in COVID-19-related myocardial injury

“…The usual limitations of EMB in diagnosing myocarditis are represented by sampling errors caused by focal or patchy involvement of myocardium as well as high interobserver variability in interpreting histopathological tissue [42]. Due to this uncertainty, several non-invasive imaging modalities are helpful in diagnosing myocarditis (Table 2) [39, [43][44][45][46].…”

A Novel Automated Method for COVID-19 Infection and Lung Segmentation using Deep Neural Networks

“…In this section, various researches in the eld of COVID-19 have been studied and analysed. Numerous studies have been conducted in the eld of medical imaging [21][22][23]. Many studies have been published which con rms the superiority of deep learning techniques over traditional machine learning methods, hand crafted methods, manual selection and texture analysis methods [24,25].…”