ReviewClinical imaging2021
SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review.
Review in Clinical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis.International journal of medical informatics · 2022Pooled it
- Review
- The 3C dataset: A comprehensive dataset for COVID-19 cardiac complications diagnosis.Data in brief · 2026Article
- The last human touch: why AI doesn't belong in life's defining moments.BMC medical ethics · 2026Article
- COVID-19 infection segmentation using hybrid deep learning and image processing techniques.Scientific reports · 2023Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- Longitudinal analyses usingAntiviral research · 2023Article
- COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19.European radiology · 2023Article
- Lung and Infection CT-Scan-Based Segmentation with 3D UNet Architecture and Its Modification.Healthcare (Basel, Switzerland) · 2023Article
- DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images.Wireless personal communications · 2023Article
- The COVID-19 in athletes (COVA) study: a national study on cardio-pulmonary involvement of SARS-CoV-2 infection among elite athletes.European clinical respiratory journal · 2023Article
- A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections.International journal of imaging systems and technology · 2022Article
- Article
- Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network.Physical and engineering sciences in medicine · 2022Article
- Pneumocystis pneumonia: An important consideration when investigating artificial intelligence-based methods in the radiological diagnosis of COVID-19.Clinical imaging · 2022Article
- Current perspective in metal oxide based photocatalysts for virus disinfection: A review.Journal of environmental management · 2022Review
- The evolving direct and indirect platforms for the detection of SARS-CoV-2.Journal of virological methods · 2022Review
- Lack of AI-based method for pneumocystis pneumonia classification in radiological diagnosis of SARS-CoV-2.Clinical imaging · 2021Article
- Review
- The Global Impact of COVID-19: Historical Development, Molecular Characterization, Drug Discovery and Future Directions.Clinical pathology (Thousand Oaks, Ventura County, Calif.)Review
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSARS-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.
methodsWe 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.
resultsCT 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.
conclusionThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.