Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

AIX-COVNET; Michael Roberts; Ghassem Gozaliasl; Jing Tang; Tolou Shadbahr

doi:10.1038/s42256-021-00307-0

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

AIX-COVNET, Michael Roberts, Ghassem Gozaliasl, Jing Tang, Tolou Shadbahr

Research output: Contribution to journal › Article › Scientific › peer-review

Original language	English
Journal	Nature Machine Intelligence
Volume	3
Issue number	3
Pages (from-to)	199-217
Number of pages	19
ISSN	2522-5839
DOIs	https://doi.org/10.1038/s42256-021-00307-0
Publication status	Published - Mar 2021
MoE publication type	A1 Journal article-refereed

Fields of Science

3122 Cancers
111 Mathematics
113 Computer and information sciences
PREDICTION
RADIOMICS
VALIDATION
IMAGES
RISK
TOOL

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10.1038/s42256-021-00307-0Licence: CC BY

s42256-021-00307-0Final published version, 1.76 MBLicence: CC BY

Cite this

@article{bad31bde56c24f3ab30263aa8c9f13ae,

title = "Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans",

keywords = "3122 Cancers, 111 Mathematics, 113 Computer and information sciences, PREDICTION, RADIOMICS, VALIDATION, IMAGES, RISK, TOOL",

author = "AIX-COVNET and Michael Roberts and Derek Driggs and Matthew Thorpe and Julian Gilbey and Michael Yeung and Stephan Ursprung and Aviles-Rivero, {Angelica I.} and Christian Etmann and Cathal McCague and Lucian Beer and Weir-McCall, {Jonathan R.} and Zhongzhao Teng and Effrossyni Gkrania-Klotsas and Alessandro Ruggiero and Anna Korhonen and Emily Jefferson and Emmanuel Ako and Georg Langs and Ghassem Gozaliasl and Guang Yang and Helmut Prosch and Jacobus Preller and Jan Stanczuk and Jing Tang and Johannes Hofmanninger and Judith Babar and S{\'a}nchez, {Lorena Escudero} and Muhunthan Thillai and Gonzalez, {Paula Martin} and Philip Teare and Xiaoxiang Zhu and Mishal Patel and Conor Cafolla and Hojjat Azadbakht and Joseph Jacob and Josh Lowe and Kang Zhang and Kyle Bradley and Marcel Wassin and Markus Holzer and Kangyu Ji and Ortet, {Maria Delgado} and Tao Ai and Nicholas Walton and Pietro Lio and Samuel Stranks and Tolou Shadbahr and Weizhe Lin and Yunfei Zha and Zhangming Niu",

year = "2021",

month = mar,

doi = "10.1038/s42256-021-00307-0",

language = "English",

volume = "3",

pages = "199--217",

journal = "Nature Machine Intelligence",

issn = "2522-5839",

publisher = "Nature Research ",

number = "3",

}

TY - JOUR

T1 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

AU - AIX-COVNET

AU - Roberts, Michael

AU - Driggs, Derek

AU - Thorpe, Matthew

AU - Gilbey, Julian

AU - Yeung, Michael

AU - Ursprung, Stephan

AU - Aviles-Rivero, Angelica I.

AU - Etmann, Christian

AU - McCague, Cathal

AU - Beer, Lucian

AU - Weir-McCall, Jonathan R.

AU - Teng, Zhongzhao

AU - Gkrania-Klotsas, Effrossyni

AU - Ruggiero, Alessandro

AU - Korhonen, Anna

AU - Jefferson, Emily

AU - Ako, Emmanuel

AU - Langs, Georg

AU - Gozaliasl, Ghassem

AU - Yang, Guang

AU - Prosch, Helmut

AU - Preller, Jacobus

AU - Stanczuk, Jan

AU - Tang, Jing

AU - Hofmanninger, Johannes

AU - Babar, Judith

AU - Sánchez, Lorena Escudero

AU - Thillai, Muhunthan

AU - Gonzalez, Paula Martin

AU - Teare, Philip

AU - Zhu, Xiaoxiang

AU - Patel, Mishal

AU - Cafolla, Conor

AU - Azadbakht, Hojjat

AU - Jacob, Joseph

AU - Lowe, Josh

AU - Zhang, Kang

AU - Bradley, Kyle

AU - Wassin, Marcel

AU - Holzer, Markus

AU - Ji, Kangyu

AU - Ortet, Maria Delgado

AU - Ai, Tao

AU - Walton, Nicholas

AU - Lio, Pietro

AU - Stranks, Samuel

AU - Shadbahr, Tolou

AU - Lin, Weizhe

AU - Zha, Yunfei

AU - Niu, Zhangming

PY - 2021/3

Y1 - 2021/3

KW - 3122 Cancers

KW - 111 Mathematics

KW - 113 Computer and information sciences

KW - PREDICTION

KW - RADIOMICS

KW - VALIDATION

KW - IMAGES

KW - RISK

KW - TOOL

U2 - 10.1038/s42256-021-00307-0

DO - 10.1038/s42256-021-00307-0

M3 - Article

SN - 2522-5839

VL - 3

SP - 199

EP - 217

JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

IS - 3

ER -