Fig. 4: The performance of the AI system in identifying COVID-19 pneumonia from CXR images. | Nature Biomedical Engineering

Fig. 4: The performance of the AI system in identifying COVID-19 pneumonia from CXR images.

From: A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images

Fig. 4: The performance of the AI system in identifying COVID-19 pneumonia from CXR images.The alternative text for this image may have been generated using AI.

a,b,d,e, ROC curves (a,d) and normalized confusion matrices (b,e) for binary classification. a,b, The performance of the AI system in differentiating between COVID-19 pneumonia and other pneumonia (‘Others’, for example, bacterial pneumonia) on the test dataset: AUC = 0.966 (95% CI = 0.955–0.975), sensitivity = 92.07%, specificity = 90.12%. d,e, The performance of the AI system in differentiating between COVID-19 pneumonia and other viral pneumonia (OVP) on the test dataset: AUC = 0.867 (95% CI = 0.828–0.902), sensitivity = 82.32%, specificity = 72.63%. c,f, ROC curves showing the performance of the AI system in identifying severe or non-severe COVID-19 from other pneumonia (c) and other types of viral pneumonia (f).

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