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10.3390/jcm12227115
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Article

Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital

by
Alexandru Stoichita
1,2,*,
Maria Ghita
3,4,
Beatrice Mahler
1,2,
Silviu Vlasceanu
1,2,
Andreea Ghinet
2,
Madalina Mosteanu
2,5,
Andreea Cioacata
2,
Andreea Udrea
6,
Alina Marcu
6,
George Daniel Mitra
6,
Clara Mihaela Ionescu
3,7 and
Adriana Iliesiu
1,8
1
Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
2
“Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania
3
Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium
4
Faculty of Medicine and Health Sciences, Antwerp University, 2610 Wilrijk, Belgium
5
Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
6
Medicai, 020961 Bucharest, Romania
7
Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
8
Clinical Hospital “Prof. Dr. Th. Burghele”, 061344 Bucharest, Romania
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(22), 7115; https://doi.org/10.3390/jcm12227115
Submission received: 19 September 2023 / Revised: 27 October 2023 / Accepted: 4 November 2023 / Published: 15 November 2023
(This article belongs to the Section Respiratory Medicine)
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Abstract

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.
Keywords: lung lesion; SARS-CoV-2; vaccination; image interpretation; artificial intelligence lung lesion; SARS-CoV-2; vaccination; image interpretation; artificial intelligence

Share and Cite

MDPI and ACS Style

Stoichita, A.; Ghita, M.; Mahler, B.; Vlasceanu, S.; Ghinet, A.; Mosteanu, M.; Cioacata, A.; Udrea, A.; Marcu, A.; Mitra, G.D.; et al. Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital. J. Clin. Med. 2023, 12, 7115. https://doi.org/10.3390/jcm12227115

AMA Style

Stoichita A, Ghita M, Mahler B, Vlasceanu S, Ghinet A, Mosteanu M, Cioacata A, Udrea A, Marcu A, Mitra GD, et al. Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital. Journal of Clinical Medicine. 2023; 12(22):7115. https://doi.org/10.3390/jcm12227115

Chicago/Turabian Style

Stoichita, Alexandru, Maria Ghita, Beatrice Mahler, Silviu Vlasceanu, Andreea Ghinet, Madalina Mosteanu, Andreea Cioacata, Andreea Udrea, Alina Marcu, George Daniel Mitra, and et al. 2023. "Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital" Journal of Clinical Medicine 12, no. 22: 7115. https://doi.org/10.3390/jcm12227115

APA Style

Stoichita, A., Ghita, M., Mahler, B., Vlasceanu, S., Ghinet, A., Mosteanu, M., Cioacata, A., Udrea, A., Marcu, A., Mitra, G. D., Ionescu, C. M., & Iliesiu, A. (2023). Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital. Journal of Clinical Medicine, 12(22), 7115. https://doi.org/10.3390/jcm12227115

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