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

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. M...

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Autores principales: Soffer, Shelly, Klang, Eyal, Shimon, Orit, Barash, Yiftach, Cahan, Noa, Greenspana, Hayit, Konen, Eli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8338977/
https://www.ncbi.nlm.nih.gov/pubmed/34349191
http://dx.doi.org/10.1038/s41598-021-95249-3
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author Soffer, Shelly
Klang, Eyal
Shimon, Orit
Barash, Yiftach
Cahan, Noa
Greenspana, Hayit
Konen, Eli
author_facet Soffer, Shelly
Klang, Eyal
Shimon, Orit
Barash, Yiftach
Cahan, Noa
Greenspana, Hayit
Konen, Eli
author_sort Soffer, Shelly
collection PubMed
description 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.
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spelling pubmed-83389772021-08-05 Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis Soffer, Shelly Klang, Eyal Shimon, Orit Barash, Yiftach Cahan, Noa Greenspana, Hayit Konen, Eli Sci Rep Article 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. Nature Publishing Group UK 2021-08-04 /pmc/articles/PMC8338977/ /pubmed/34349191 http://dx.doi.org/10.1038/s41598-021-95249-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Soffer, Shelly
Klang, Eyal
Shimon, Orit
Barash, Yiftach
Cahan, Noa
Greenspana, Hayit
Konen, Eli
Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8338977/
https://www.ncbi.nlm.nih.gov/pubmed/34349191
http://dx.doi.org/10.1038/s41598-021-95249-3
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