The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning
Open accessAbstract
Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model's ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online.
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“6 Saliency maps In order to ensure that the models are looking at reasonable aspects of the images [20-22], a saliency map is computed by computing the gradient of the output prediction with respect to the input image (if a pixel is changed, how much will it change the prediction).”
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Cited by
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044
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Applications of artificial intelligence in battling against covid-19: A literature review
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A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
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Artificial intelligence in medicine and the disclosure of risks
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Metadata
- Authors
- Cohen, J. P., Dao, L., Roth, K., Morrison, P., Bengio, Y., Abbasi, A. F., Shen, B., Mahsa, H. K., Ghassemi, M., Li, H., & Duong, T.
- Venue
- Cureus
- Published
- 2020-07-28
- Volume
- 12
- Issue
- 7
- Pages
- e9448-e9448
- ISSN
- 2168-8184 (ISSN-L)
- OpenAlex
- W3027380610
- Cited by
- 259
- References
- 28
- FWCI
- 24.32
- Type
- journal article
- Language
- en
- Open access
- diamond
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