Background/Objectives: To determine the inter-rater agreement of visual and AI-based assessments of a renowned semi-quantitative chest CT scoring system (Pan-score) used to evaluate the severity of pulmonary involvement (e.g., ground-glass opacities, consolidations) in patients suffering from COVID-19. Methods: This retrospective study includes patients with PCR-confirmed COVID-19, who received a chest CT scan (not more than three days prior to or after the positive PCR test) between 21 March 2020 and 30 December 2022. The five lung lobes were scored separately on a scale from 0 (no pulmonary involvement) to 5 (>75% pulmonary involvement) by a radiology specialist, an experienced assistant physician, a medical student, and a dedicated AI-based software tool for chest CT. Weighted Cohen’s κ values were calculated to assess the reliability of agreement between the different readers. Results: A total of 569 consecutive patients (381 males [67.0%], 188 females [33.0%]; mean age 68.8 years) with confirmed COVID-19 were evaluated. All of them received at least one chest CT scan. There was a significant difference (p < 0.001) between the mean Pan-score evaluated by the three human readers (9.35 ± 6.03) and the score computed fully automatically by the software (10.44 ± 5.10). However, the inter-rater agreement both between the three different human readers and between the human readers and the AI was high throughout, with κ values of 0.71–0.86 and 0.83, respectively. The slice thickness of the reconstructed CT images did not have an impact on the inter-rater agreement, but the total score was significantly higher when the images were acquired following the administration of i. v. contrast media. Conclusions: The evaluated chest CT scoring system is user-friendly due to its simplicity, though it is generally prone to inaccuracies, since the estimation of the extent of pulmonary involvement is quite subjective. Nevertheless, the inter-rater agreement was high throughout, both between the differently experienced human readers and between the human readers and the AI software. In summary, the Pan-score seems to be a reliable approach to estimate the extent of pulmonary involvement in patients suffering from COVID-19.

Background/Objectives: Investigation of whether visual and AI-based assessments of the severity of COVID-19 pneumonia using an established semi-quantitative chest CT scoring system (Pan score) correlate with laboratory parameters as well as pulmonary function, and of the score’s diagnostic value in predicting the patients’ clinical outcome. Methods: This retrospective analysis comprises patients with PCR-confirmed COVID-19 who received a chest CT scan (not more than three days prior to or after the positive PCR test) between 12 August 2020, and 30 December 2022. Each of the five lung lobes was assessed separately using a scoring system ranging from 0 (no pulmonary involvement) to 5 (>75% pulmonary involvement) by a radiology specialist, an experienced resident physician, a medical student, and a dedicated AI-based chest CT software tool. In addition, pulmonary function and laboratory parameters, the duration of ICU stays and of any required mechanical ventilation, as well as the clinical outcome (discharge vs. death) were recorded, and their correlation with the obtained CT score was analysed. Statistical analyses comprised descriptive baseline comparisons using non-parametric tests, bivariate correlation matrices, and ROC curves to assess diagnostic accuracy. Furthermore, multivariable logistic, ordinal, and age-adjusted spline regression models were constructed to calculate odds ratios and estimate predicted probabilities for cumulative ICU and mechanical ventilation duration thresholds. Results: In total, 351 consecutive patients with confirmed COVID-19 (223 males [63.5%], 128 females [36.5%]; mean age 67.0 years) were included, all of whom underwent at least one chest CT scan. Compared with patients who were discharged, deceased patients had a significantly (p < 0.05) higher mean Pan score (11.7 ± 6.0 vs. 8.8 ± 5.0), higher rates of mechanical ventilation (61.3 vs. 33.0%), and both a higher incidence (42.7 vs. 25.4%) and longer duration (10.5 [6.0–20.0] vs. 6.0 [3.0–10.0] days) of ICU stays. The Pan score showed a moderate and consistent association with the requirement for mechanical ventilation (ρ = 0.49; q < 0.001) and the duration of the ICU stay (ρ = 0.43; q < 0.001). Conclusions: The investigated semi-quantitative CT score is a simple, reliable tool for assessing the extent of COVID-19 pneumonia and can be evaluated both by radiologists and fully automated AI software. While its predictive value for all-cause mortality was only moderate, it showed good performance in predicting the need for and duration of mechanical ventilation, as well as intensive care requirement.

Background/Objectives: Investigation of whether visual and AI-based assessments of the severity of COVID-19 pneumonia using an established semi-quantitative chest CT scoring system (Pan score) correlate with laboratory parameters as well as pulmonary function, and of the score’s diagnostic value in predicting the patients’ clinical outcome. Methods: This retrospective analysis comprises patients with PCR-confirmed COVID-19, who received a chest CT scan (not more than three days prior to or after the positive PCR test) between March 21, 2020, and December 27, 2021. Each of the five lung lobes was assessed separately using a scoring system ranging from 0 (no pulmonary involvement) to 5 (> 75% pulmonary involvement) by a radiology specialist, an experienced resident physician, a medical student, and a dedicated AI-based chest CT software tool. In addition, pulmonary function and laboratory parameters, the duration of ICU stays and of any required mechanical ventilation, as well as the clinical outcome (discharge vs. death) were recorded, and their correlation with the obtained CT score was analysed. Statistical analyses comprised descriptive baseline comparisons using non-parametric tests, bivariate correlation matrices, and ROC curves to assess diagnostic accuracy. Furthermore, multivariable logistic, ordinal, and age-adjusted spline regression models were constructed to calculate odds ratios and estimate predicted probabilities for cumulative ICU and mechanical ventilation duration thresholds. Results: In total, 351 consecutive patients with confirmed COVID-19 (223 males [63.5%], 128 females [36.5%]; mean age 67.0 years) were included, all of whom underwent at least one chest CT scan. Compared with patients who were discharged, deceased patients had a significantly (p < 0.05) higher mean Pan score (11.7 ± 6.0 vs. 8.8 ± 5.0), higher rates of mechanical ventilation (56.0 vs. 32.1 %), and both a higher incidence (42.7 vs. 25.4 %) and longer duration (10.5 [6.0–20.0] vs. 6.0 [3.0–10.0] days) of ICU stays. The Pan score showed a strong and consistent association with the requirement for mechanical ventilation (r = 0.54; p < 0.01) and the duration of the ICU stay (r = 0.39; p < 0.01). Conclusions: The investigated semi-quantitative CT score is a simple, reliable tool for assessing the extent of COVID-19 pneumonia and can be evaluated both by radiologists and fully automated AI software. While its predictive value for all-cause mortality was only moderate, it showed good performance in predicting the need for and duration of mechanical ventilation, as well as intensive care requirement.