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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- Source :
- Roberts, M, Driggs, D, Thorpe, M, Gilbey, J, Yeung, M, Ursprung, S, Aviles-Rivero, A, Etmann, C, McCague, C, Beer, L, Weir-McCall, J, Teng, Z, Gkrania-Klotsas, E, AIX-COVNET, Rudd, J, Sala, E & Schoenlieb, C-B 2021, ' Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans ', Nature Machine Intelligence, vol. 3, pp. 199-217 . https://doi.org/10.1038/s42256-021-00307-0
- Publication Year :
- 2021
-
Abstract
- Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and 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 search EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from January 1, 2020 to October 3, 2020 which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 61 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher quality model development and well documented manuscripts.<br />35 pages, 3 figures, 2 tables, updated to the period 1 January 2020 - 3 October 2020
- Subjects :
- 639/705/1042
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
analysis
PREDICTION
Radiography
Computer Vision and Pattern Recognition (cs.CV)
cs.LG
Computer Science - Computer Vision and Pattern Recognition
Computed tomography
computer.software_genre
030218 nuclear medicine & medical imaging
Machine Learning (cs.LG)
0302 clinical medicine
Radiomics
Statistics - Machine Learning
111 Mathematics
TOOL
cs.CV
RISK
medicine.diagnostic_test
Image and Video Processing (eess.IV)
stat.ML
3. Good health
Computer Vision and Pattern Recognition
RADIOMICS
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19)
Computer Networks and Communications
IMAGES
3122 Cancers
MEDLINE
Machine Learning (stat.ML)
Machine learning
VALIDATION
03 medical and health sciences
Artificial Intelligence
692/53/2422
medicine
692/53/2421
FOS: Electrical engineering, electronic engineering, information engineering
Model development
631/326/596/4130
business.industry
Diagnostic marker
Electrical Engineering and Systems Science - Image and Video Processing
113 Computer and information sciences
Human-Computer Interaction
eess.IV
Artificial intelligence
business
Computational science Diagnostic markers Prognostic markers SARS-CoV-2
computer
030217 neurology & neurosurgery
Software
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Roberts, M, Driggs, D, Thorpe, M, Gilbey, J, Yeung, M, Ursprung, S, Aviles-Rivero, A, Etmann, C, McCague, C, Beer, L, Weir-McCall, J, Teng, Z, Gkrania-Klotsas, E, AIX-COVNET, Rudd, J, Sala, E & Schoenlieb, C-B 2021, ' Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans ', Nature Machine Intelligence, vol. 3, pp. 199-217 . https://doi.org/10.1038/s42256-021-00307-0
- Accession number :
- edsair.doi.dedup.....6203f7e866d10f75cc7e536c8041e1e0