Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
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...
Main Authors: | 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 |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-01-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-020-79470-0 |
Similar Items
-
Deep learning in chest radiography: Detection of findings and presence of change.
by: Ramandeep Singh, et al.
Published: (2018-01-01) -
Clinical and imaging features predict mortality in COVID-19 infection in Iran.
by: Fatemeh Homayounieh, et al.
Published: (2020-01-01) -
Deploying Clinical Process Improvement Strategies to Reduce Motion Artifacts and Expiratory Phase Scanning in Chest CT
by: Ruhani Doda Khera, et al.
Published: (2019-08-01) -
Multifactorial Analysis of Mortality in Screening Detected Lung Cancer
by: Subba R. Digumarthy, et al.
Published: (2018-01-01) -
Radiation dose reduction in chest dual-energy computed tomography: effect on image quality and diagnostic information
by: Rodrigo Canellas, et al.
Published: (2018-11-01)