Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results

PURPOSE: To evaluate whether early chest computed tomography (CT) lesions quantified by an artificial intelligence (AI)-based commercial software and blood test values at the initial presentation can differentiate the severity of COVID-19 pneumonia. MATERIALS AND METHODS: This retrospective study included 100 SARS-CoV-2-positive patients with mild (n = 23), moderate (n = 37) or severe (n = 40) pneumonia classified according to the Japanese guidelines. Univariate Kruskal–Wallis and multivariate ordinal logistic analyses were used to examine whether CT parameters (opacity score, volume of opacity, % opacity, volume of high opacity, % high opacity and mean HU total on CT) as well as blood test parameters [procalcitonin, estimated glomerular filtration rate (eGFR), C-reactive protein, % lymphocyte, ferritin, aspartate aminotransferase, lactate dehydrogenase, alanine aminotransferase, creatine kinase, hemoglobin A1c, prothrombin time, activated partial prothrombin time (APTT), white blood cell count and creatinine] differed by disease severity. RESULTS: All CT parameters and all blood test parameters except procalcitonin and APPT were significantly different among mild, moderate and severe groups. By multivariate analysis, mean HU total and eGFR were two independent factors associated with severity (p < 0.0001). Cutoff values for mean HU total and eGFR were, respectively, − 801 HU and 77 ml/min/1.73 m(2) between mild and moderate pneumonia and − 704 HU and 53 ml/min/1.73 m(2) between moderate and severe pneumonia. CONCLUSION: The mean HU total of the whole lung, determined by the AI algorithm, and eGFR reflect the severity of COVID-19 pneumonia.