Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

Abstract Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE o...

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Autores principales:	Shelly Soffer, Eyal Klang, Orit Shimon, Yiftach Barash, Noa Cahan, Hayit Greenspana, Eli Konen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/652f12799e464eab8a41b8a92c1cf18a
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spelling	oai:doaj.org-article:652f12799e464eab8a41b8a92c1cf18a2021-12-02T14:53:48ZDeep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis10.1038/s41598-021-95249-32045-2322https://doaj.org/article/652f12799e464eab8a41b8a92c1cf18a2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95249-3https://doaj.org/toc/2045-2322Abstract Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.Shelly SofferEyal KlangOrit ShimonYiftach BarashNoa CahanHayit GreenspanaEli KonenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shelly Soffer Eyal Klang Orit Shimon Yiftach Barash Noa Cahan Hayit Greenspana Eli Konen Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis
description	Abstract Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.
format	article
author	Shelly Soffer Eyal Klang Orit Shimon Yiftach Barash Noa Cahan Hayit Greenspana Eli Konen
author_facet	Shelly Soffer Eyal Klang Orit Shimon Yiftach Barash Noa Cahan Hayit Greenspana Eli Konen
author_sort	Shelly Soffer
title	Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis
title_short	Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis
title_full	Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis
title_fullStr	Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis
title_full_unstemmed	Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis
title_sort	deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/652f12799e464eab8a41b8a92c1cf18a
work_keys_str_mv	AT shellysoffer deeplearningforpulmonaryembolismdetectiononcomputedtomographypulmonaryangiogramasystematicreviewandmetaanalysis AT eyalklang deeplearningforpulmonaryembolismdetectiononcomputedtomographypulmonaryangiogramasystematicreviewandmetaanalysis AT oritshimon deeplearningforpulmonaryembolismdetectiononcomputedtomographypulmonaryangiogramasystematicreviewandmetaanalysis AT yiftachbarash deeplearningforpulmonaryembolismdetectiononcomputedtomographypulmonaryangiogramasystematicreviewandmetaanalysis AT noacahan deeplearningforpulmonaryembolismdetectiononcomputedtomographypulmonaryangiogramasystematicreviewandmetaanalysis AT hayitgreenspana deeplearningforpulmonaryembolismdetectiononcomputedtomographypulmonaryangiogramasystematicreviewandmetaanalysis AT elikonen deeplearningforpulmonaryembolismdetectiononcomputedtomographypulmonaryangiogramasystematicreviewandmetaanalysis
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Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

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