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Title
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Imagistic findings using artificial intelligence in vaccinated versus unvaccinated SARS-CoV-2-positive patients receiving in-care treatment at a tertiary lung hospital
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Author
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Abstract
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| Background: In December 2019 the World Health Organization announced that the widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection had become a global pandemic. The most affected organ by the novel virus is the lung, and imaging exploration of the thorax using computer tomography (CT) scanning and X-ray has had an important impact. Materials and Methods: We assessed the prevalence of lung lesions in vaccinated versus unvaccinated SARS-CoV-2 patients using an artificial intelligence (AI) platform provided by Medicai. The software analyzes the CT scans, performing the lung and lesion segmentation using a variant of the U-net convolutional network. Results: We conducted a cohort study at a tertiary lung hospital in which we included 186 patients: 107 (57.52%) male and 59 (42.47%) females, of which 157 (84.40%) were not vaccinated for SARS-CoV-2. Over five times more unvaccinated patients than vaccinated ones are admitted to the hospital and require imaging investigations. More than twice as many unvaccinated patients have more than 75% of the lungs affected. Patients in the age group 30-39 have had the most lung lesions at almost 69% of both lungs affected. Compared to vaccinated patients with comorbidities, unvaccinated patients with comorbidities had developed increased lung lesions by 5%. Conclusion: The study revealed a higher percentage of lung lesions among unvaccinated SARS-CoV-2-positive patients admitted to The National Institute of Pulmonology "Marius Nasta" in Bucharest, Romania, underlining the importance of vaccination and also the usefulness of artificial intelligence in CT interpretation. |
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Language
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English
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Source (journal)
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Journal of Clinical Medicine
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Publication
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2023
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ISSN
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2077-0383
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DOI
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10.3390/JCM12227115
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Volume/pages
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12
:22
(2023)
, p. 1-11
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Article Reference
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7115
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ISI
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001113578400001
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Pubmed ID
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38002725
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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