From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases

Abstract

Background: The current study aimed to investigate the distribution and extent of lung involvement in patients with COVID-19, assess the relationship between lung involvement and the need for intensive care unit (ICU) admission and compare the performance of computer analysis with the judgment of radiological experts. 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 computed tomography (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 analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against the expert rating provided by a radiological expert. Results: The results showed a higher degree of infiltration and collapse in the left lower lobe compared to the upper lobe (p < 0.05) and a similar pattern in the right lower 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 lobes demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, no significant difference in COVID-19 involvement was found when comparing the anterior and posterior lungs. The middle third displayed greater COVID-19 involvement than the upper and lower thirds. Patients requiring ICU admission during their inpatient 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 expert rating by radiological experts. Conclusion: The findings suggest that the extent of lung involvement, particularly in the lower lobes and middle third, 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.