A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019

Ito, Rintaro; Iwano, Shingo; Naganawa, Shinji

doi:10.5152/dir.2019.20294

Cited by 42 publications

(38 citation statements)

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“…In the short period since the outbreak, many groups have already developed AI algorithms for diagnosing COVID-19. Shi et al( 29 ) and Ito et al( 30 ) provide overviews of the proposed approaches. Most studies analyze small data sets and employ two-dimensional (2D) neural network architectures on axial sections.…”

Section: Discussionmentioning

confidence: 99%

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

et al. 2021

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“…The collection of numerous CT images has opened the possibility to build a database of pulmonary images from COVID-19 patients. Interestingly, the recent progress in integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging revealed that AI could be used to support disease diagnosis [133,134]. Ito et al reviewed the literature on the use of AI for lung diagnostic imaging of COVID-19 patients.…”

Section: Ct Scans and Serology Methodsmentioning

confidence: 99%

“…The number of datasets ranged from 106 to 5941, with sensitivities ranging from 67–100% and specificities ranging from 81–100% for prediction of COVID-19 pneumonia. This study revealed the usefulness of AI approach to support the diagnosis of COVID-19, but also for future emerging diseases [ 134 ]. All the collected knowledge on lung lesions revealed some characteristic CT findings of COVID-19 pneumonia: the pulmonary ground-glass opacities in a peripheral distribution and the consolidation referring to an increase in pulmonary parenchymal density [ 135 , 136 , 137 ].…”

Section: Diagnosismentioning

confidence: 99%

The Global Emergency of Novel Coronavirus (SARS-CoV-2): An Update of the Current Status and Forecasting

Hozhabri

Piceci‐Sparascio

Sohrabi

et al. 2020

IJERPH

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Over the past two decades, there have been two major outbreaks where the crossover of animal Betacoronaviruses to humans has resulted in severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). In December 2019, a global public health concern started with the emergence of a new strain of coronavirus (SARS-CoV-2 or 2019 novel coronavirus, 2019-nCoV) which has rapidly spread all over the world from its origin in Wuhan, China. SARS-CoV-2 belongs to the Betacoronavirus genus, which includes human SARS-CoV, MERS and two other human coronaviruses (HCoVs), HCoV-OC43 and HCoV-HKU1. The fatality rate of SARS-CoV-2 is lower than the two previous coronavirus epidemics, but it is faster spreading and the large number of infected people with severe viral pneumonia and respiratory illness, showed SARS-CoV-2 to be highly contagious. Based on the current published evidence, herein we summarize the origin, genetics, epidemiology, clinical manifestations, preventions, diagnosis and up to date treatments of SARS-CoV-2 infections in comparison with those caused by SARS-CoV and MERS-CoV. Moreover, the possible impact of weather conditions on the transmission of SARS-CoV-2 is also discussed. Therefore, the aim of the present review is to reconsider the two previous pandemics and provide a reference for future studies as well as therapeutic approaches.

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“…ML and DL technologies are being applied during the COVID-19 pandemic to characterize lung CT images [ [166] , [167] , [168] ] with varying degrees of success. Kang et al [ 169 ] applied representation learning to characterize non-infected chest CT scans from COVID-19 patients with an accuracy of 95.5% .…”

Section: Machine Learning and Deep Learning For Tissue Characterizatimentioning

confidence: 99%

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

Suri¹,

Puvvula²,

Biswas³

et al. 2020

Computers in Biology and Medicine

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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.

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A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019

Cited by 42 publications

References 0 publications

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

The Global Emergency of Novel Coronavirus (SARS-CoV-2): An Update of the Current Status and Forecasting

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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