Quantitative Evaluation of COVID-19 Pneumonia CT Using AI Analysis-Feasibility and Differentiation from Other Common Pneumonia Forms

• Published in: Diagnostics (Basel) 2023-06, Vol.13 (12), p.2129
• Main Authors: Ebong, Una, Büttner, Susanne Martina, Schmidt, Stefan A, Flack, Franziska, Korf, Patrick, Peters, Lynn, Grüner, Beate, Stenger, Steffen, Stamminger, Thomas, Kestler, Hans, Beer, Meinrad, Kloth, Christopher
• Format: Article
• Language: English
• Subjects: Artificial intelligence; automated segmentation and quantification; Bacterial pneumonia; Coronaviruses; COVID-19; COVID-19 pneumonia; Development and progression; Diagnosis; Medical imaging; Medical research; Medicine, Experimental; Pneumonia
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13122129

To implement the technical feasibility of an AI-based software prototype optimized for the detection of COVID-19 pneumonia in CT datasets of the lung and the differentiation between other etiologies of pneumonia. This single-center retrospective case-control-study consecutively yielded 144 patients (58 female, mean age 57.72 ± 18.25 y) with CT datasets of the lung. Subgroups including confirmed bacterial ( = 24, 16.6%), viral ( = 52, 36.1%), or fungal ( = 25, 16.6%) pneumonia and ( = 43, 30.7%) patients without detected pneumonia (comparison group) were evaluated using the AI-based . Scoring (extent, etiology) was compared to reader assessment. The software achieved an optimal sensitivity of 80.8% with a specificity of 50% for the detection of COVID-19; however, the human radiologist achieved optimal sensitivity of 80.8% and a specificity of 97.2%. The mean postprocessing time was 7.61 ± 4.22 min. The use of a contrast agent did not influence the results of the software ( = 0.81). The mean evaluated COVID-19 probability is 0.80 ± 0.36 significantly higher in COVID-19 patients than in patients with fungal pneumonia ( < 0.05) and bacterial pneumonia ( < 0.001). The mean percentage of opacity (PO) and percentage of high opacity (PHO ≥ -200 HU) were significantly higher in COVID-19 patients than in healthy patients. However, the total mean HU in COVID-19 patients was -679.57 ± 112.72, which is significantly higher than in the healthy control group ( < 0.001). The detection and quantification of pneumonia beyond the primarily trained COVID-19 datasets is possible and shows comparable results for COVID-19 pneumonia to an experienced reader. The advantages are the fast, automated segmentation and quantification of the pneumonia foci.