The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System
Open accessAbstract
Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years ± 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. © RSNA, 2020
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients
Salehi, S., Abedi, A., Balakrishnan, S., & Gholamrezanezhad, A. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.23034 · Cited by 1,600
Rubin, G. D., Ryerson, C. J., Haramati, L. B., Sverzellati, N., Kanne, J. P., Raoof, S., Schluger, N. W., Volpi, A., Yim, J.-J., Martin, I. B. K., et al. (2020) · Radiology · doi:10.1148/radiol.2020201365 · Cited by 1,101
Self-citationAutomated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks
Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., & Montana, G. (2019) · Radiology · doi:10.1148/radiol.2018180921 · Cited by 303
Deep Learning for Chest Radiograph Diagnosis in the Emergency Department
Hwang, E. J., Nam, J. G., Lim, W. H., Park, S. J., Jeong, Y. S., Kang, J. H., Hong, E. K., Kim, T. M., Goo, J. M., Park, S., et al. (2019) · Radiology · doi:10.1148/radiol.2019191225 · Cited by 164
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Metadata
- Authors
- Murphy, K., Smits, H., Knoops, A. J. G., Korst, M. B. J. M., Samson, T., Scholten, E. T., Schalekamp, S., Schaefer-Prokop, C. M., Philipsen, R. H. H. M., Meijers, A., Melendez, J., van Ginneken, B., & Rutten, M.
- Venue
- Radiology
- Published
- 2020-09
- Volume
- 296
- Issue
- 3
- Pages
- E166-E172
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W3021214012
- Cited by
- 214
- References
- 5
- FWCI
- 20.28
- Type
- journal article
- Language
- en
- Open access
- green
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- Murphy, K., Smits, H., Knoops, A. J. G., Korst, M. B. J. M., Samson, T., Scholten, E. T., Schalekamp, S., Schaefer-Prokop, C. M., Philipsen, R. H. H. M., Meijers, A., Melendez, J., van Ginneken, B., & Rutten, M. (2020). COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System. Radiology. https://doi.org/10.1148/radiol.2020201874
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