Radiology· 2020Q1
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy
- 2,025citations
- Q1SCImago
- 2020year
Short summary
A deep learning model achieved 95% accuracy (AUC) in detecting COVID-19 and differentiating it from community-acquired pneumonia (CAP) and other lung conditions on pulmonary CT scans.
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Key points
- Deep learning model accurately detects COVID-19 on CT scans.
- Model differentiates COVID-19 from community-acquired pneumonia (CAP) and other lung conditions.
- Achieved 87% sensitivity and 92% specificity for CAP detection in an independent test set.
- Overall diagnostic accuracy (AUC) was 0.95.
AI-generated from the title and abstract; the full text is not read.
Abstract
< .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020
The authors' abstract, as published at the source. Radiology, 2020 · DOI ↗
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Field: Radiology, Nuclear Medicine and Imaging
Radiology, Nuclear Medicine and ImagingMedicine