ArticleJournal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures)2025
Artificial intelligence algorithms based approach in evaluating COVID-19 patients and management.
Article in Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Myocardial Injury and Predictors of In-Hospital Mortality in Severe or Critical COVID-19: A Single-Center Observational Cohort Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Smart Cardiac ICU: Digital Integration, Predictive Analytics, and Perioperative Inflammation.Bioengineering (Basel, Switzerland) · 2026Review
- Diagnostic Performance of Artificial Intelligence in Detecting COVID-19 Pneumonia on Chest Imaging.Cureus · 2026Review
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9 authors.
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Abstract
Introduction: 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. Aim of the study: 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. Methods: 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. Results: 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<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). Conclusions: 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.
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