BibTex format
@article{Roberts:2021:10.1038/s42256-021-00307-0,
author = {Roberts, M and Driggs, D and Thorpe, M and Gilbey, J and Yeung, M and Ursprung, S and Aviles-Rivero, AI and Etmann, C and McCague, C and Beer, L and Weir-McCall, JR and Teng, Z and Gkrania-Klotsas, E and Ruggiero, A and Korhonen, A and Jefferson, E and Ako, E and Langs, G and Gozaliasl, G and Yang, G and Prosch, H and Preller, J and Stanczuk, J and Tang, J and Hofmanninger, J and Babar, J and Sánchez, LE and Thillai, M and Gonzalez, PM and Teare, P and Zhu, X and Patel, M and Cafolla, C and Azadbakht, H and Jacob, J and Lowe, J and Zhang, K and Bradley, K and Wassin, M and Holzer, M and Ji, K and Ortet, MD and Ai, T and Walton, N and Lio, P and Stranks, S and Shadbahr, T and Lin, W and Zha, Y and Niu, Z and Rudd, JHF and Sala, E and Schönlieb, CB},
doi = {10.1038/s42256-021-00307-0},
journal = {Nature Machine Intelligence},
pages = {199--217},
title = {Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans},
url = {http://dx.doi.org/10.1038/s42256-021-00307-0},
volume = {3},
year = {2021}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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 entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 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.
AU - Roberts,M
AU - Driggs,D
AU - Thorpe,M
AU - Gilbey,J
AU - Yeung,M
AU - Ursprung,S
AU - Aviles-Rivero,AI
AU - Etmann,C
AU - McCague,C
AU - Beer,L
AU - Weir-McCall,JR
AU - Teng,Z
AU - Gkrania-Klotsas,E
AU - Ruggiero,A
AU - Korhonen,A
AU - Jefferson,E
AU - Ako,E
AU - Langs,G
AU - Gozaliasl,G
AU - Yang,G
AU - Prosch,H
AU - Preller,J
AU - Stanczuk,J
AU - Tang,J
AU - Hofmanninger,J
AU - Babar,J
AU - Sánchez,LE
AU - Thillai,M
AU - Gonzalez,PM
AU - Teare,P
AU - Zhu,X
AU - Patel,M
AU - Cafolla,C
AU - Azadbakht,H
AU - Jacob,J
AU - Lowe,J
AU - Zhang,K
AU - Bradley,K
AU - Wassin,M
AU - Holzer,M
AU - Ji,K
AU - Ortet,MD
AU - Ai,T
AU - Walton,N
AU - Lio,P
AU - Stranks,S
AU - Shadbahr,T
AU - Lin,W
AU - Zha,Y
AU - Niu,Z
AU - Rudd,JHF
AU - Sala,E
AU - Schönlieb,CB
DO - 10.1038/s42256-021-00307-0
EP - 217
PY - 2021///
SP - 199
TI - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
T2 - Nature Machine Intelligence
UR - http://dx.doi.org/10.1038/s42256-021-00307-0
VL - 3
ER -
