| Autor: |
Shadi Ebrahimian, Fatemeh Homayounieh, Marcio A. B. C. Rockenbach, Preetham Putha, Tarun Raj, Ittai Dayan, Bernardo C. Bizzo, Varun Buch, Dufan Wu, Kyungsang Kim, Quanzheng Li, Subba R. Digumarthy, Mannudeep K. Kalra |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-020-79470-0 |
| Popis: |
Abstract To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79–0.86; p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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