The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data. Patients with COVID-19 who underwent chest computed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU). A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP 9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia. Both SQNLP and VQAS were significantly related to the clinical findings, highlighting their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.

Eur J Breast Health AVES | Copyright © 2017 Türkiye Meme Hastalıkları Dernekleri Federasyonu |

Objective: To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course. Methods: From March 11 to April 15, 2020, 51 patients (age, 18–84 years; 28 men) diagnosed and hospitalized with COVID-19 pneumonia with a total of 116 CT scans were enrolled in the study. Patients were divided into mild (n = 12), moderate (n = 31), and severe (n = 8) groups based on clinical severity. An AI-based quantitative CT analysis, including lung volume, opacity score, opacity volume, percentage of opacity, and mean lung density, was performed in initial and follow-up CTs obtained at different time points. Receiver operating characteristic analysis was performed to find the diagnostic ability of quantitative CT parameters for discriminating severe from nonsevere pneumonia. Results: In baseline assessment, the severe group had significantly higher opacity score, opacity volume, higher percentage of opacity, and higher mean lung density than the moderate group (all P ≤ 0.001). Through consecutive time points, the severe group had a significant decrease in lung volume (P = 0.006), a significant increase in total opacity score (P = 0.003), and percentage of opacity (P = 0.007). A significant increase in total opacity score was also observed for the mild group (P = 0.011). Residual opacities were observed in all groups. The involvement of more than 4 lobes (sensitivity, 100%; specificity, 65.26%), total opacity score greater than 4 (sensitivity, 100%; specificity, 64.21), total opacity volume greater than 337.4 mL (sensitivity, 80.95%; specificity, 84.21%), percentage of opacity greater than 11% (sensitivity, 80.95%; specificity, 88.42%), total high opacity volume greater than 10.5 mL (sensitivity, 95.24%; specificity, 66.32%), percentage of high opacity greater than 0.8% (sensitivity, 85.71%; specificity, 80.00%) and mean lung density HU greater than −705 HU (sensitivity, 57.14%; specificity, 90.53%) were related to severe pneumonia. Conclusions: An AI-based quantitative CT analysis is an objective tool in demonstrating disease severity and can also assist the clinician in follow-up by providing information about the disease course and prognosis according to different clinical severity groups.

Eur J Breast Health AVES | Copyright © 2017 Türkiye Meme Hastalıkları Dernekleri Federasyonu |

Background/Objectives: Artificial intelligence (AI) increasingly supports chest radiograph interpretation, but per-abnormality comparisons of commercial tools using CT-anchored reference standards remain limited. We compared two commercial AI tools using a CT-anchored, CXR-targeted reference framework. Methods: We retrospectively reviewed chest radiographs obtained between 30 October 2023 and 10 October 2024. Each radiograph was paired with CT performed within 14 days or within 2 days for rapidly progressive findings. Seven abnormalities across five analysis categories were evaluated: fracture, nodule, pleural effusion, pneumothorax, and airspace disease (edema, atelectasis, opacity/consolidation). Radiologists first confirmed abnormalities on CT then assessed their visibility on the paired radiograph. Outputs from vendors A and B were recorded. Sensitivity was calculated for CT-confirmed, radiographically visible abnormalities; specificity, precision, and accuracy were calculated within the same detectability framework. Vendors were compared using McNemar’s test. Results: The cohort included 1707 radiographs from 1680 patients (mean age, 60.3 ± 16.5 years; 50.5% male). Of 3573 CT-confirmed abnormalities, 1981 (55.4%) were radiographically visible and formed the primary reference-positive set. Vendor A had higher sensitivity for fractures, nodules, and pleural effusions (all p < 0.001), whereas vendor B had higher specificity for these findings (all p < 0.05). For pneumothorax, vendor B showed numerically higher sensitivity but lower specificity and accuracy; case numbers were limited, and precision was low for both tools. No significant differences were observed for harmonized airspace disease. Overall specificity was high, while sensitivity and precision varied across abnormalities. Conclusions: Both tools showed abnormality-specific strengths and limitations, supporting tailored implementation and real-world clinical validation.