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

Tutkimustuotos: Artikkelijulkaisu › Artikkeli › Tieteellinen › vertaisarvioitu

Alkuperäiskieli	englanti
Lehti	Nature Machine Intelligence
Vuosikerta	3
Numero	3
Sivut	199-217
Sivumäärä	19
ISSN	2522-5839
DOI - pysyväislinkit	https://doi.org/10.1038/s42256-021-00307-0
Tila	Julkaistu - maalisk. 2021
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Tieteenalat

3122 Syöpätaudit
111 Matematiikka
113 Tietojenkäsittely- ja informaatiotieteet

Pääsy asiakirjaan

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

s42256-021-00307-0Lopullinen julkaistu versio, 1,76 MBLisenssi: CC BY

Siteeraa tätä

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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",

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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

VL - 3

SP - 199

EP - 217

JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

SN - 2522-5839

IS - 3

ER -