{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T20:00:50Z","timestamp":1789588850161,"version":"build-2803163510"},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"3","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>COVID-19 pneumonia manifests with a wide range of clinical symptoms, from minor flu-like signs to multi-organ failure. Chest computed tomography (CT) is the most effective imaging method for assessing the extent of the pulmonary lesions and correlates with disease severity. Increased workloads during the COVID-19 pandemic led to the development of various artificial intelligence tools to enable quicker diagnoses and quantitative evaluations of the lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim of the study<\/jats:title>\n                    <jats:p>This study aims to analyse the correlation between lung lesions identified on CT scans and the biological inflammatory markers assessed, to establish the survival rate among patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 120 patients diagnosed with moderate to severe COVID-19 pneumonia who were admitted to the intensive care unit and the internal medicine department between September 2020 and October 2021. Each patient underwent a chest CT scan, which was subsequently analysed by two radiologists and an AI post-processing software. On the same day, blood was collected from the patients to determine inflammatory markers. The markers analysed in this study include the neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio, platelet-lymphocyte ratio, systemic immune-inflammatory index, systemic inflammation response index, systemic inflammation index, and serum interleukin-6 value.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>There were strong and very strong correlations between the derived inflammatory markers, interleukin-6, and the CT severity scores obtained by the AI algorithm (r=0.851, p&lt;0.001 in the case of NLR). Higher values of the inflammatory markers and high lung opacity scores correlated with a decreased survival rate. Crazy paving was also associated with an increased risk of mortality (OR=2.89, p=0.006).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI-based chest CT analysis plays a crucial role in assessing patients with COVID-19 pneumonia. When combined with inflammatory markers, it provides a reliable and objective method for evaluating COVID-19 pneumonia, enhancing the accuracy of diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2478\/jccm-2025-0032","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T19:57:44Z","timestamp":1754337464000},"page":"247-256","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence algorithms based approach in evaluating COVID-19 patients and management"],"prefix":"10.2478","volume":"11","author":[{"given":"Ioana","family":"H\u0103lmaciu","sequence":"first","affiliation":[{"name":"Department of Radiology , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anca Meda","family":"V\u0103sie\u0219iu","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases , George Emil Palade University of Medicine Pharmacy Science, and Technology of Targu Mures , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Manea","sequence":"additional","affiliation":[{"name":"Department of Radiology , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"},{"name":"Doctoral School of Medicine and Pharmacy , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Dragomir","sequence":"additional","affiliation":[{"name":"Doctoral School of Medicine and Pharmacy , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioana","family":"Tripon","sequence":"additional","affiliation":[{"name":"Faculty of Medicine , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vlad","family":"Vunvulea","sequence":"additional","affiliation":[{"name":"Department of Anatomy , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian","family":"Boeriu","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Rus","sequence":"additional","affiliation":[{"name":"Department of Radiology , Clinical Emergency County Hospital of Targu Mure\u0219 , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minodora","family":"Dobreanu","sequence":"additional","affiliation":[{"name":"Center for Advanced Medical and Pharmaceutical Research , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"},{"name":"Department of Laboratory Medicine , George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures , Targu Mures , Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"2025080421072958552_j_jccm-2025-0032_ref_001","doi-asserted-by":"crossref","unstructured":"Montoro FS, Gordo MLP, Tasc\u00f3n \u00c1D, De Gracia MM, Velez SO, Fern\u00e1ndez SF, et al. Quantification of pulmonary opacities using artificial intelligence in chest CT scans during SARS-CoV-2 pandemic: validation and prognostic assessment. Egypt J Radiol Nucl Med. 2023;54:156.","DOI":"10.1186\/s43055-023-01105-x"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_002","doi-asserted-by":"crossref","unstructured":"Xu X, Yu C, Qu J, Zhang L, Jiang S, Huang D, et al. Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur J Nucl Med Mol Imaging. 2020;47:1275\u201380.","DOI":"10.1007\/s00259-020-04735-9"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_003","unstructured":"Use of laboratory methods for SARS diagnosis [Internet]. [cited 2025 Apr 9]. Available from: https:\/\/www.who.int\/publications\/m\/item\/use-of-laboratory-methods-for-sars-diagnosis"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_004","doi-asserted-by":"crossref","unstructured":"Ippolito D, Ragusi M, Gandola D, Maino C, Pecorelli A, Terrani S, et al. Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia. Eur Radiol. 2021;31:2726\u201336.","DOI":"10.1007\/s00330-020-07271-0"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_005","doi-asserted-by":"crossref","unstructured":"Chan JF-W, Yip CC-Y, To KK-W, Tang TH-C, Wong SC-Y, Leung K-H, et al. Improved Molecular Diagnosis of COVID-19 by the Novel, Highly Sensitive and Specific COVID-19-RdRp\/Hel Real-Time Reverse Transcription-PCR Assay Validated In Vitro and with Clinical Specimens. J Clin Microbiol. 2020;58:e00310\u201320.","DOI":"10.1128\/JCM.00310-20"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_006","doi-asserted-by":"crossref","unstructured":"Li B, Li X, Wang Y, Han Y, Wang Y, Wang C, et al. Diagnostic value and key features of computed tomography in Coronavirus Disease 2019. Emerg Microbes Infect. 9:787\u201393.","DOI":"10.1080\/22221751.2020.1750307"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_007","doi-asserted-by":"crossref","unstructured":"Xie J, Tong Z, Guan X, Du B, Qiu H, Slutsky AS. Critical care crisis and some recommendations during the COVID-19 epidemic in China. Intensive Care Med. 2020;46:837\u201340.","DOI":"10.1007\/s00134-020-05979-7"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_008","doi-asserted-by":"crossref","unstructured":"Grassi R, Belfiore MP, Montanelli A, Patelli G, Urraro F, Giacobbe G, et al. COVID-19 pneumonia: computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT). Radiol med. 2021;126:553\u201360.","DOI":"10.1007\/s11547-020-01305-9"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_009","doi-asserted-by":"crossref","unstructured":"Mossa-Basha M, Meltzer CC, Kim DC, Tuite MJ, Kolli KP, Tan BS. Radiology Department Preparedness for COVID-19: Radiology Scientific Expert Review Panel. Radiology [Internet]. 2020 [cited 2025 Apr 9]; Available from: https:\/\/pubs.rsna.org\/doi\/10.1148\/radiol.2020200988","DOI":"10.1148\/radiol.2020200988"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_010","doi-asserted-by":"crossref","unstructured":"Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus Disease 2019 (COVID-19): A Perspective from China. Radiology. 2020;296:E15\u201325.","DOI":"10.1148\/radiol.2020200490"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_011","doi-asserted-by":"crossref","unstructured":"Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology. 2020;295:202\u20137.","DOI":"10.1148\/radiol.2020200230"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_012","doi-asserted-by":"crossref","unstructured":"Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology. 2020;295:200463.","DOI":"10.1148\/radiol.2020200463"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_013","doi-asserted-by":"crossref","unstructured":"Arb\u0103na\u0219i EM, Halmaciu I, Kaller R, Mure\u0219an AV, Arb\u0103na\u0219i EM, Suciu BA, et al. Systemic Inflammatory Biomarkers and Chest CT Findings as Predictors of Acute Limb Ischemia Risk, Intensive Care Unit Admission, and Mortality in COVID-19 Patients. Diagnostics (Basel). 2022;12:2379.","DOI":"10.3390\/diagnostics12102379"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_014","doi-asserted-by":"crossref","unstructured":"Karaaslan T, Karaaslan E. Predictive Value of Systemic Immune-inflammation Index in Determining Mortality in COVID-19 Patients. J Crit Care Med (Targu Mures). 2022;8:156\u201364.","DOI":"10.2478\/jccm-2022-0013"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_015","doi-asserted-by":"crossref","unstructured":"Li Y, Xia L. Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. AJR Am J Roentgenol. 2020;214:1280\u20136.","DOI":"10.2214\/AJR.20.22954"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_016","doi-asserted-by":"crossref","unstructured":"Lei J, Li J, Li X, Qi X. CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology. 2020;295:18.","DOI":"10.1148\/radiol.2020200236"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_017","doi-asserted-by":"crossref","unstructured":"Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine. 2020;382:1708\u201320.","DOI":"10.1056\/NEJMoa2002032"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_018","doi-asserted-by":"crossref","unstructured":"Caruso D, Zerunian M, Polici M, Pucciarelli F, Polidori T, Rucci C, et al. Chest CT Features of COVID-19 in Rome, Italy. Radiology. 2020;296:E79\u201385.","DOI":"10.1148\/radiol.2020201237"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_019","doi-asserted-by":"crossref","unstructured":"Tian S, Xiong Y, Liu H, Niu L, Guo J, Liao M, et al. Pathological study of the 2019 novel coronavirus disease (COVID-19) through postmortem core biopsies. Mod Pathol. 2020;33:1007\u201314.","DOI":"10.1038\/s41379-020-0536-x"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_020","doi-asserted-by":"crossref","unstructured":"Wang Y, Dong C, Hu Y, Li C, Ren Q, Zhang X, et al. Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study. Radiology. 2020;296:E55\u201364.","DOI":"10.1148\/radiol.2020200843"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_021","doi-asserted-by":"crossref","unstructured":"Chabi ML, Dana O, Kennel T, Gence-Breney A, Salvator H, Ballester MC, et al. Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia. Diagnostics. 2021;11:878.","DOI":"10.3390\/diagnostics11050878"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_022","doi-asserted-by":"crossref","unstructured":"Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054\u201362.","DOI":"10.1016\/S0140-6736(20)30566-3"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_023","doi-asserted-by":"crossref","unstructured":"Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol. 2021;31:6096\u2013104.","DOI":"10.1007\/s00330-021-07715-1"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_024","doi-asserted-by":"crossref","unstructured":"Citu C, Gorun OM, Motoc A, Citu IM, Gorun F, Malita D. Correlation of Lung Damage on CT Scan with Laboratory Inflammatory Markers in COVID-19 Patients: A Single-Center Study from Romania. Journal of Clinical Medicine. 2022;11:4299.","DOI":"10.3390\/jcm11154299"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_025","doi-asserted-by":"crossref","unstructured":"Di Meglio L, Carriero S, Biondetti P, Wood BJ, Carrafiello G. Chest imaging in patients with acute respiratory failure because of coronavirus disease 2019. Curr Opin Crit Care. 2022;28:17\u201324.","DOI":"10.1097\/MCC.0000000000000906"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_026","doi-asserted-by":"crossref","unstructured":"Kanne JP. Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist. Radiology. 2020;295:16\u20137.","DOI":"10.1148\/radiol.2020200241"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_027","doi-asserted-by":"crossref","unstructured":"Li J, Yan R, Zhai Y, Qi X, Lei J. Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review. Diagn Interv Radiol. 2021;27:621\u201332.","DOI":"10.5152\/dir.2020.20212"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_028","doi-asserted-by":"crossref","unstructured":"Grassi R, Cappabianca S, Urraro F, Feragalli B, Montanelli A, Patelli G, et al. Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software. International Journal of Environmental Research and Public Health. 2020;17:6914.","DOI":"10.3390\/ijerph17186914"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_029","doi-asserted-by":"crossref","unstructured":"Contreras-Grande J, Pineda-Borja V, D\u00edaz H, Calderon-Anyosa RJc, Rodr\u00edguez B, Mor\u00f3n M. CHEST COMPUTED TOMOGRAPHY FINDINGS ASSOCIATED WITH SEVERITY AND MORTALITY IN PATIENTS WITH COVID-19. Rev Peru Med Exp Salud Publica. 2021;38:206\u201313.","DOI":"10.17843\/rpmesp.2021.382.6562"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_030","doi-asserted-by":"crossref","unstructured":"Chon Y, Kim JY, Suh YJ, Lee JY, Park JS, Moon SM, et al. Adverse Initial CT Findings Associated with Poor Prognosis of Coronavirus Disease. J Korean Med Sci. 2020;35:e316.","DOI":"10.3346\/jkms.2020.35.e316"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_031","doi-asserted-by":"crossref","unstructured":"Stoian M, Roman A, Boeriu A, Oni\u0219or D, Bandila SR, Bab\u0103 DF, et al. Long-Term Radiological Pulmonary Changes in Mechanically Ventilated Patients with Respiratory Failure due to SARS-CoV-2 Infection. Biomedicines. 2023;11:2637.","DOI":"10.3390\/biomedicines11102637"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_032","doi-asserted-by":"crossref","unstructured":"Lassau N, Ammari S, Chouzenoux E, Gortais H, Herent P, Devilder M, et al. Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat Commun. 2021;12:634.","DOI":"10.1038\/s41467-020-20657-4"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_033","doi-asserted-by":"crossref","unstructured":"Liu T, Zhang J, Yang Y, Ma H, Li Z, Zhang J, et al. The role of interleukin-6 in monitoring severe case of coronavirus disease 2019. EMBO Mol Med. 2020;12:e12421.","DOI":"10.15252\/emmm.202012421"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_034","doi-asserted-by":"crossref","unstructured":"Ahmed IM, Hefny HM, Ali AH, Refaie BM, Abdelal KM, Taha EM. Serum interleukin-6 as a predictor of the severity of coronavirus disease 2019. Research and Opinion in Anesthesia & Intensive Care. 2023;10:68\u201374.","DOI":"10.4103\/roaic.roaic_22_22"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_035","doi-asserted-by":"crossref","unstructured":"Laguna-Goya R, Utrero-Rico A, Talayero P, Lasa-Lazaro M, Ramirez-Fernandez A, Naranjo L, et al. IL-6\u2013based mortality risk model for hospitalized patients with COVID-19. Journal of Allergy and Clinical Immunology. 2020;146:799\u2013807.e9.","DOI":"10.1016\/j.jaci.2020.07.009"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_036","doi-asserted-by":"crossref","unstructured":"Man MA, Rajnoveanu R-M, Motoc NS, Bondor CI, Chis AF, Lesan A, et al. Neutrophil-to-lymphocyte ratio, platelets-to-lymphocyte ratio, and eosinophils correlation with high-resolution computer tomography severity score in COVID-19 patients. PLoS One. 2021;16:e0252599.","DOI":"10.1371\/journal.pone.0252599"},{"key":"2025080421072958552_j_jccm-2025-0032_ref_037","doi-asserted-by":"crossref","unstructured":"Najjar R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics. 2023;13:2760.","DOI":"10.3390\/diagnostics13172760"}],"container-title":["The Journal of Critical Care Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.sciendo.com\/pdf\/10.2478\/jccm-2025-0032","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T21:07:54Z","timestamp":1754341674000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.2478\/jccm-2025-0032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,7,31]]},"published-print":{"date-parts":[[2025,7,1]]}},"alternative-id":["10.2478\/jccm-2025-0032"],"URL":"https:\/\/doi.org\/10.2478\/jccm-2025-0032","relation":{},"ISSN":["2393-1817"],"issn-type":[{"value":"2393-1817","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]}}}