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Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study
BACKGROUND: Objective to preliminarily verify the feasibility of AI intelligent diagnosis of pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary artery CT angiography (CTA). METHO...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184458/ https://www.ncbi.nlm.nih.gov/pubmed/34164472 http://dx.doi.org/10.21037/atm-21-975 |
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author | Li, Xiang Wang, Xiang Yang, Xin Lin, Yi Huang, Zengfa |
author_facet | Li, Xiang Wang, Xiang Yang, Xin Lin, Yi Huang, Zengfa |
author_sort | Li, Xiang |
collection | PubMed |
description | BACKGROUND: Objective to preliminarily verify the feasibility of AI intelligent diagnosis of pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary artery CT angiography (CTA). METHODS: Computed tomography angiography (CTA) data of 85 patients with PE in our hospital from January 2017 to May 2018 were retrospectively collected and randomly allocated to2 groups: computer depth learning group (n=43) and experimental group (n=42). For the training set (13,144 sheets) and the test set (313 sheets), the auxiliary diagnosis method was obtained and applied to the experimental group. RESULTS: Among the participants, a good sensitivity of 90.9% and an average false positive of 2.0 were obtained by using the deep learning detection method proposed in this paper, and the detection rate was positively correlated with arterial grade. CONCLUSIONS: The computer-aided diagnostic method proposed in this paper can effectively improve the detection rate of PE, especially for the detection of intra-arterial embolism above grade 3. However, because of the high misdetection rate, more in-depth learning datasets are needed for the detection of embolism below grade 3. |
format | Online Article Text |
id | pubmed-8184458 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-81844582021-06-22 Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study Li, Xiang Wang, Xiang Yang, Xin Lin, Yi Huang, Zengfa Ann Transl Med Original Article BACKGROUND: Objective to preliminarily verify the feasibility of AI intelligent diagnosis of pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary artery CT angiography (CTA). METHODS: Computed tomography angiography (CTA) data of 85 patients with PE in our hospital from January 2017 to May 2018 were retrospectively collected and randomly allocated to2 groups: computer depth learning group (n=43) and experimental group (n=42). For the training set (13,144 sheets) and the test set (313 sheets), the auxiliary diagnosis method was obtained and applied to the experimental group. RESULTS: Among the participants, a good sensitivity of 90.9% and an average false positive of 2.0 were obtained by using the deep learning detection method proposed in this paper, and the detection rate was positively correlated with arterial grade. CONCLUSIONS: The computer-aided diagnostic method proposed in this paper can effectively improve the detection rate of PE, especially for the detection of intra-arterial embolism above grade 3. However, because of the high misdetection rate, more in-depth learning datasets are needed for the detection of embolism below grade 3. AME Publishing Company 2021-05 /pmc/articles/PMC8184458/ /pubmed/34164472 http://dx.doi.org/10.21037/atm-21-975 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Li, Xiang Wang, Xiang Yang, Xin Lin, Yi Huang, Zengfa Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study |
title | Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study |
title_full | Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study |
title_fullStr | Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study |
title_full_unstemmed | Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study |
title_short | Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study |
title_sort | preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184458/ https://www.ncbi.nlm.nih.gov/pubmed/34164472 http://dx.doi.org/10.21037/atm-21-975 |
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