Artificial intelligence algorithms based approach in evaluating COVID-19 patients and management.

IF 1.7 Q4 CRITICAL CARE MEDICINE
Journal of Critical Care Medicine Pub Date : 2025-07-31 eCollection Date: 2025-07-01 DOI:10.2478/jccm-2025-0032
Ioana Hălmaciu, Anca Meda Văsieșiu, Andrei Manea, Andrei Dragomir, Ioana Tripon, Vlad Vunvulea, Cristian Boeriu, Andrea Rus, Minodora Dobreanu
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引用次数: 0

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.

Abstract Image

Abstract Image

基于人工智能算法的COVID-19患者评估与管理方法
COVID-19肺炎表现为广泛的临床症状,从轻微的流感样体征到多器官衰竭。胸部计算机断层扫描(CT)是评估肺部病变程度最有效的成像方法,与疾病严重程度相关。COVID-19大流行期间工作量的增加促使开发了各种人工智能工具,以便更快地诊断和定量评估病变。研究目的:本研究旨在分析CT扫描发现的肺部病变与评估的生物炎症标志物之间的相关性,以确定患者的生存率。方法:本回顾性研究纳入2020年9月至2021年10月在重症监护室和内科收治的120例诊断为中重度COVID-19肺炎的患者。每位患者都接受了胸部CT扫描,随后由两名放射科医生和人工智能后处理软件进行分析。同日,采集患者血液,测定炎症标志物。本研究分析的指标包括中性粒细胞-淋巴细胞比率(NLR)、单核细胞-淋巴细胞比率、血小板-淋巴细胞比率、全身免疫-炎症指数、全身炎症反应指数、全身炎症指数和血清白细胞介素-6值。结果:所得炎症标志物、白细胞介素-6与AI算法获得的CT严重程度评分存在强相关性和非常强相关性(r=0.851, p)。结论:基于AI的胸部CT分析在评估COVID-19肺炎患者中具有至关重要的作用。与炎症标志物联合使用,为评估COVID-19肺炎提供了可靠、客观的方法,提高了诊断的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Critical Care Medicine
Journal of Critical Care Medicine CRITICAL CARE MEDICINE-
CiteScore
2.00
自引率
9.10%
发文量
21
审稿时长
11 weeks
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