Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: COVID lung involvement; Coronavirus disease 2019 (COVID-19); artificial intelligence-supported computed tomography computer analysis (AI-supported CT computer analysis); clinical decision-making; forecast of intensive care unit admission (forecast of ICU admission).

Figure 1

This scatter plot shows the percentage of COVID-affected samples under two conditions: uncalibrated and calibrated. The data points are color-coded based on their respective conditions, with green representing the calibrated condition and orange representing the uncalibrated condition. Additionally, lines connect the data points within each pairing, illustrating the change in affected percentage between the two conditions. The plot employs a white background theme, with the x-axis labeled “Condition” and the y-axis labeled “Affected (%)”. The x-axis displays the two conditions in a discrete manner, with the order of the categories set manually. The color legend is omitted from the plot for clarity. COVID, coronavirus disease.

Figure 2

Case example: patient #78 lung CT segmentation of trachea, lungs, and lobes in calibrated scalar volume. R, axial view; Y, sagittal view; G, coronal view; CT, computed tomography.

Figure 3

Case example: patient #78 after lung CT analysis of calibrated scalar volume with signs of severe SARS-CoV-2 affection (>50% in both lungs). Green: emphysema; blue: normal lung; orange: infiltration; pink: collapse; orange + pink: affected; red: vessels. CT, computed tomography; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Figure 4

This box and whisker plot shows the even distribution of COVID affected lung CT with no difference between biological sexes. M, male; F, female; COVID, coronavirus disease; CT, computed tomography.

Figure 5

Correlation between baseline clinical score (by an expert radiologist) and percentage of COVID-affected lung derived by automatic computer analysis (R=0.86, P<2.2e−16). COVID, coronavirus disease.

Figure 6

This box-and-whisker plot was created to visualize the distribution of percentages of COVID affection in the expert score groups. All scores showed significantly different values with the exception of score 1 vs. 2. *, P<0.05. n.s., not significant; COVID, coronavirus disease.

Figure 7

The scatter plot displays scores from two reviewers. Green lines represent identical scores, while orange lines show diverging scores (31 of 81, 38%). The second reviewer excluded three CT scans (score = “NA”: 1 pneumothorax, 2 pre-existent lung diseases). The Wilcoxon signed rank test with continuity correction revealed a significant interobserver bias (V =356, P=0.001). CT, computed tomography; NA, not available; V, variant of the test statistic.

Figure 8

This box-and-whisker plot shows the distribution of the percentage of affected samples among patients needing or not to be admitted to the ICU. This is represented on the x-axis, while the y-axis displays the percentage of COVID-affected lung volume. Within each ICU category, a boxplot summarizes the median, quartiles, and outliers of the affected percentage. Jittered individual data points are overlaid on the boxplots, visually representing the underlying data distribution. Patients who needed ICU support had significantly higher percentages of COVID alterations in both lungs (P<0.05). ICU, intensive care unit; COVID, coronavirus disease.