PDF(Pubmed)
Abstract：
UNASSIGNED: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists\' assessments.
UNASSIGNED: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts.
UNASSIGNED: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts.
UNASSIGNED: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.
UNASSIGNED: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts.
UNASSIGNED: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts.
UNASSIGNED: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.
摘要：
■2019年全球冠状病毒病（COVID-19）大流行给医疗保健系统带来了巨大挑战，特别是对胸部计算机断层扫描（CT）扫描的需求增加，缺乏自动化分析。我们的研究通过利用人工智能支持的自动计算机分析来调查COVID-19患者的肺部受累分布和程度来解决这一问题。此外,我们探讨了肺部受累与重症监护病房（ICU）入院之间的关系，同时还将计算机分析性能与放射科专家的评估进行比较。
■共有81名来自开源COVID数据库的确诊COVID-19感染的患者被纳入研究。排除了3名患者。使用CT扫描评估了78例患者的肺部受累情况，并且对不同肺叶和区域的浸润和塌陷程度进行了量化。分析了肺部受累与ICU入院之间的关系。此外,将COVID-19受累的计算机分析与放射学专家提供的人体评级进行了比较。
■结果表明，与上叶相比，下叶的浸润和塌陷程度更高（P<0.05）。在COVID-19相关的左右下叶受累中没有检测到显著差异。右中叶受累程度低于右下叶（P<0.05）。在检查区域时，当比较后部与后部时，发现明显更多的COVID-19参与前半部和下半部与肺的上半部分。患者,根据计算机分析，在治疗期间需要入住ICU的患者肺实质中的COVID-19受累率明显更高，与留在普通病房的患者相比。参与COVID-19超过40%的患者几乎完全在重症监护中接受治疗。计算机检测COVID-19感染与放射学专家的评级之间存在高度相关性。
■研究结果表明，肺部受累的程度，特别是在下裂片，背肺,肺的下半部分，可能与COVID-19患者需要入住ICU有关。计算机分析显示与专家评级高度相关，强调其在临床环境中评估肺部受累的潜在效用。这些信息可能有助于指导正在进行或将来的大流行期间的临床决策和资源分配。有必要进行更大样本量的进一步研究以验证这些发现。
■共有81名来自开源COVID数据库的确诊COVID-19感染的患者被纳入研究。排除了3名患者。使用CT扫描评估了78例患者的肺部受累情况，并且对不同肺叶和区域的浸润和塌陷程度进行了量化。分析了肺部受累与ICU入院之间的关系。此外,将COVID-19受累的计算机分析与放射学专家提供的人体评级进行了比较。
■结果表明，与上叶相比，下叶的浸润和塌陷程度更高（P<0.05）。在COVID-19相关的左右下叶受累中没有检测到显著差异。右中叶受累程度低于右下叶（P<0.05）。在检查区域时，当比较后部与后部时，发现明显更多的COVID-19参与前半部和下半部与肺的上半部分。患者,根据计算机分析，在治疗期间需要入住ICU的患者肺实质中的COVID-19受累率明显更高，与留在普通病房的患者相比。参与COVID-19超过40%的患者几乎完全在重症监护中接受治疗。计算机检测COVID-19感染与放射学专家的评级之间存在高度相关性。
■研究结果表明，肺部受累的程度，特别是在下裂片，背肺,肺的下半部分，可能与COVID-19患者需要入住ICU有关。计算机分析显示与专家评级高度相关，强调其在临床环境中评估肺部受累的潜在效用。这些信息可能有助于指导正在进行或将来的大流行期间的临床决策和资源分配。有必要进行更大样本量的进一步研究以验证这些发现。
