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Review
. 2021 Aug:76:6-14.
doi: 10.1016/j.clinimag.2021.01.019. Epub 2021 Jan 28.

SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review

Affiliations
Review

SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review

Narjes Benameur et al. Clin Imaging. 2021 Aug.

Abstract

Objective: SARS-CoV-2 is a worldwide health emergency with unrecognized clinical features. This paper aims to review the most recent medical imaging techniques used for the diagnosis of SARS-CoV-2 and their potential contributions to attenuate the pandemic. Recent researches, including artificial intelligence tools, will be described.

Methods: We review the main clinical features of SARS-CoV-2 revealed by different medical imaging techniques. First, we present the clinical findings of each technique. Then, we describe several artificial intelligence approaches introduced for the SARS-CoV-2 diagnosis.

Results: CT is the most accurate diagnostic modality of SARS-CoV-2. Additionally, ground-glass opacities and consolidation are the most common signs of SARS-CoV-2 in CT images. However, other findings such as reticular pattern, and crazy paving could be observed. We also found that pleural effusion and pneumothorax features are less common in SARS-CoV-2. According to the literature, the B lines artifacts and pleural line irregularities are the common signs of SARS-CoV-2 in ultrasound images. We have also stated the different studies, focusing on artificial intelligence tools, to evaluate the SARS-CoV-2 severity. We found that most of the reported works based on deep learning focused on the detection of SARS-CoV-2 from medical images while the challenge for the radiologists is how to differentiate between SARS-CoV-2 and other viral infections with the same clinical features.

Conclusion: The identification of SARS-CoV-2 manifestations on medical images is a key step in radiological workflow for the diagnosis of the virus and could be useful for researchers working on computer-aided diagnosis of pulmonary infections.

Keywords: Artificial intelligence; Chest CT; Clinical findings; Medical imaging techniques; SARS-CoV-2.

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Conflict of interest statement

There is no conflict of interest.

Figures

Fig. 1
Fig. 1
A 71-year-old female, with SARS-CoV-2, CXR shows extensive parenchymal peripheral opacities in all pulmonary lobes.
Fig. 2
Fig. 2
A 73-year-old female, with SARS-CoV-2, presenting fever and worsening cough. Axial non-enhanced CT scan shows GGO and initial fine linear reticulations in the left and right lobes.
Fig. 3
Fig. 3
A 57 year-old male with SARS-CoV-2: (a) CT scan demonstrated an initial consolidation in the superior left lobe and a reticular pattern superimposed on the GGO, which the sign of crazy paving pattern, (b) Sagittal view non-enhanced CT scan reveals peripheral focal ground glass opacity in the left upper lobe; (c) 3 days after admission, a follow-up CT scan shows worsening multifocal GGO with extensive interlobular thickening.
Fig. 4
Fig. 4
A 63-year-old male with SARS-CoV-2: (a) CT scan shows mosaic distribution of GGO in all pulmonary lobes (b) after three weeks of intubation, a follow-up CT scan shows extensive peripheral fibrosis linear pattern.

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