Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

Abstract Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January...

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Autores principales:	Ravi Aggarwal, Viknesh Sounderajah, Guy Martin, Daniel S. W. Ting, Alan Karthikesalingam, Dominic King, Hutan Ashrafian, Ara Darzi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/fd25beaf97d74f9280c25347f7b195a2
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spelling	oai:doaj.org-article:fd25beaf97d74f9280c25347f7b195a22021-12-02T14:26:07ZDiagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis10.1038/s41746-021-00438-z2398-6352https://doaj.org/article/fd25beaf97d74f9280c25347f7b195a22021-04-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00438-zhttps://doaj.org/toc/2398-6352Abstract Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC’s ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC’s ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC’s ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.Ravi AggarwalViknesh SounderajahGuy MartinDaniel S. W. TingAlan KarthikesalingamDominic KingHutan AshrafianAra DarziNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-23 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Ravi Aggarwal Viknesh Sounderajah Guy Martin Daniel S. W. Ting Alan Karthikesalingam Dominic King Hutan Ashrafian Ara Darzi Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
description	Abstract Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC’s ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC’s ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC’s ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.
format	article
author	Ravi Aggarwal Viknesh Sounderajah Guy Martin Daniel S. W. Ting Alan Karthikesalingam Dominic King Hutan Ashrafian Ara Darzi
author_facet	Ravi Aggarwal Viknesh Sounderajah Guy Martin Daniel S. W. Ting Alan Karthikesalingam Dominic King Hutan Ashrafian Ara Darzi
author_sort	Ravi Aggarwal
title	Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
title_short	Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
title_full	Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
title_fullStr	Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
title_full_unstemmed	Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
title_sort	diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fd25beaf97d74f9280c25347f7b195a2
work_keys_str_mv	AT raviaggarwal diagnosticaccuracyofdeeplearninginmedicalimagingasystematicreviewandmetaanalysis AT vikneshsounderajah diagnosticaccuracyofdeeplearninginmedicalimagingasystematicreviewandmetaanalysis AT guymartin diagnosticaccuracyofdeeplearninginmedicalimagingasystematicreviewandmetaanalysis AT danielswting diagnosticaccuracyofdeeplearninginmedicalimagingasystematicreviewandmetaanalysis AT alankarthikesalingam diagnosticaccuracyofdeeplearninginmedicalimagingasystematicreviewandmetaanalysis AT dominicking diagnosticaccuracyofdeeplearninginmedicalimagingasystematicreviewandmetaanalysis AT hutanashrafian diagnosticaccuracyofdeeplearninginmedicalimagingasystematicreviewandmetaanalysis AT aradarzi diagnosticaccuracyofdeeplearninginmedicalimagingasystematicreviewandmetaanalysis
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Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

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