{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T22:30:32Z","timestamp":1791325832217,"version":"4.3.1"},"reference-count":32,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Special Research Fund of Ghent University","award":["01D15919"],"award-info":[{"award-number":["01D15919"]}]},{"name":"Special Research Fund of Ghent University","award":["01J01619"],"award-info":[{"award-number":["01J01619"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>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\u201339 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 \u201cMarius Nasta\u201d in Bucharest, Romania, underlining the importance of vaccination and also the usefulness of artificial intelligence in CT interpretation.<\/jats:p>","DOI":"10.3390\/jcm12227115","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T10:57:46Z","timestamp":1700045866000},"page":"7115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1115-2379","authenticated-orcid":false,"given":"Alexandru","family":"Stoichita","sequence":"first","affiliation":[{"name":"Faculty of Medicine, University of Medicine and Pharmacy \u201cCarol Davila\u201d, 050474 Bucharest, Romania"},{"name":"\u201cMarius Nasta\u201d Institute of Pneumology, 050159 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3463-5173","authenticated-orcid":false,"given":"Maria","family":"Ghita","sequence":"additional","affiliation":[{"name":"Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium"},{"name":"Faculty of Medicine and Health Sciences, Antwerp University, 2610 Wilrijk, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9240-922X","authenticated-orcid":false,"given":"Beatrice","family":"Mahler","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Medicine and Pharmacy \u201cCarol Davila\u201d, 050474 Bucharest, Romania"},{"name":"\u201cMarius Nasta\u201d Institute of Pneumology, 050159 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3856-7917","authenticated-orcid":false,"given":"Silviu","family":"Vlasceanu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Medicine and Pharmacy \u201cCarol Davila\u201d, 050474 Bucharest, Romania"},{"name":"\u201cMarius Nasta\u201d Institute of Pneumology, 050159 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreea","family":"Ghinet","sequence":"additional","affiliation":[{"name":"\u201cMarius Nasta\u201d Institute of Pneumology, 050159 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5127-5774","authenticated-orcid":false,"given":"Madalina","family":"Mosteanu","sequence":"additional","affiliation":[{"name":"\u201cMarius Nasta\u201d Institute of Pneumology, 050159 Bucharest, Romania"},{"name":"Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreea","family":"Cioacata","sequence":"additional","affiliation":[{"name":"\u201cMarius Nasta\u201d Institute of Pneumology, 050159 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreea","family":"Udrea","sequence":"additional","affiliation":[{"name":"Medicai, 020961 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alina","family":"Marcu","sequence":"additional","affiliation":[{"name":"Medicai, 020961 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George Daniel","family":"Mitra","sequence":"additional","affiliation":[{"name":"Medicai, 020961 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7685-035X","authenticated-orcid":false,"given":"Clara Mihaela","family":"Ionescu","sequence":"additional","affiliation":[{"name":"Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium"},{"name":"Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriana","family":"Iliesiu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Medicine and Pharmacy \u201cCarol Davila\u201d, 050474 Bucharest, Romania"},{"name":"Clinical Hospital \u201cProf. Dr. Th. Burghele\u201d, 061344 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 18). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. 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