Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
AstraZeneca (United Kingdom) · University of Cambridge · +6 more institutions
Abstract
Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all…
Citation impact
- FWCI
- 85.27
- Percentile
- 100%
- References
- 112
Authors
16Topics & keywords
- Coronavirus disease 2019 (COVID-19)
- Medicine
- MEDLINE
- Medical physics
- 2019-20 coronavirus outbreak
- Machine learning
- Radiography
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)