Cargando…

A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis

We undertook a systematic review of the diagnostic accuracy of artificial intelligence-based software for identification of radiologic abnormalities (computer-aided detection, or CAD) compatible with pulmonary tuberculosis on chest x-rays (CXRs). We searched four databases for articles published bet...

Descripción completa

Detalles Bibliográficos
Autores principales: Harris, Miriam, Qi, Amy, Jeagal, Luke, Torabi, Nazi, Menzies, Dick, Korobitsyn, Alexei, Pai, Madhukar, Nathavitharana, Ruvandhi R., Ahmad Khan, Faiz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6719854/
https://www.ncbi.nlm.nih.gov/pubmed/31479448
http://dx.doi.org/10.1371/journal.pone.0221339
_version_ 1783447995200569344
author Harris, Miriam
Qi, Amy
Jeagal, Luke
Torabi, Nazi
Menzies, Dick
Korobitsyn, Alexei
Pai, Madhukar
Nathavitharana, Ruvandhi R.
Ahmad Khan, Faiz
author_facet Harris, Miriam
Qi, Amy
Jeagal, Luke
Torabi, Nazi
Menzies, Dick
Korobitsyn, Alexei
Pai, Madhukar
Nathavitharana, Ruvandhi R.
Ahmad Khan, Faiz
author_sort Harris, Miriam
collection PubMed
description We undertook a systematic review of the diagnostic accuracy of artificial intelligence-based software for identification of radiologic abnormalities (computer-aided detection, or CAD) compatible with pulmonary tuberculosis on chest x-rays (CXRs). We searched four databases for articles published between January 2005-February 2019. We summarized data on CAD type, study design, and diagnostic accuracy. We assessed risk of bias with QUADAS-2. We included 53 of the 4712 articles reviewed: 40 focused on CAD design methods (“Development” studies) and 13 focused on evaluation of CAD (“Clinical” studies). Meta-analyses were not performed due to methodological differences. Development studies were more likely to use CXR databases with greater potential for bias as compared to Clinical studies. Areas under the receiver operating characteristic curve (median AUC [IQR]) were significantly higher: in Development studies AUC: 0.88 [0.82–0.90]) versus Clinical studies (0.75 [0.66–0.87]; p-value 0.004); and with deep-learning (0.91 [0.88–0.99]) versus machine-learning (0.82 [0.75–0.89]; p = 0.001). We conclude that CAD programs are promising, but the majority of work thus far has been on development rather than clinical evaluation. We provide concrete suggestions on what study design elements should be improved.
format Online
Article
Text
id pubmed-6719854
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-67198542019-09-16 A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis Harris, Miriam Qi, Amy Jeagal, Luke Torabi, Nazi Menzies, Dick Korobitsyn, Alexei Pai, Madhukar Nathavitharana, Ruvandhi R. Ahmad Khan, Faiz PLoS One Research Article We undertook a systematic review of the diagnostic accuracy of artificial intelligence-based software for identification of radiologic abnormalities (computer-aided detection, or CAD) compatible with pulmonary tuberculosis on chest x-rays (CXRs). We searched four databases for articles published between January 2005-February 2019. We summarized data on CAD type, study design, and diagnostic accuracy. We assessed risk of bias with QUADAS-2. We included 53 of the 4712 articles reviewed: 40 focused on CAD design methods (“Development” studies) and 13 focused on evaluation of CAD (“Clinical” studies). Meta-analyses were not performed due to methodological differences. Development studies were more likely to use CXR databases with greater potential for bias as compared to Clinical studies. Areas under the receiver operating characteristic curve (median AUC [IQR]) were significantly higher: in Development studies AUC: 0.88 [0.82–0.90]) versus Clinical studies (0.75 [0.66–0.87]; p-value 0.004); and with deep-learning (0.91 [0.88–0.99]) versus machine-learning (0.82 [0.75–0.89]; p = 0.001). We conclude that CAD programs are promising, but the majority of work thus far has been on development rather than clinical evaluation. We provide concrete suggestions on what study design elements should be improved. Public Library of Science 2019-09-03 /pmc/articles/PMC6719854/ /pubmed/31479448 http://dx.doi.org/10.1371/journal.pone.0221339 Text en © 2019 Harris et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Harris, Miriam
Qi, Amy
Jeagal, Luke
Torabi, Nazi
Menzies, Dick
Korobitsyn, Alexei
Pai, Madhukar
Nathavitharana, Ruvandhi R.
Ahmad Khan, Faiz
A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis
title A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis
title_full A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis
title_fullStr A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis
title_full_unstemmed A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis
title_short A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis
title_sort systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6719854/
https://www.ncbi.nlm.nih.gov/pubmed/31479448
http://dx.doi.org/10.1371/journal.pone.0221339
work_keys_str_mv AT harrismiriam asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT qiamy asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT jeagalluke asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT torabinazi asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT menziesdick asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT korobitsynalexei asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT paimadhukar asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT nathavitharanaruvandhir asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT ahmadkhanfaiz asystematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT harrismiriam systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT qiamy systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT jeagalluke systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT torabinazi systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT menziesdick systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT korobitsynalexei systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT paimadhukar systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT nathavitharanaruvandhir systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis
AT ahmadkhanfaiz systematicreviewofthediagnosticaccuracyofartificialintelligencebasedcomputerprogramstoanalyzechestxraysforpulmonarytuberculosis