Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/57068 |
Resumo: | © The Author(s) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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/. |
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Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning modelArtificial IntelligenceCoronary angiographyCoronary artery diseaseDeep learningMachine learningPercutaneous coronary intervention© The Author(s) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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/.Introduction: We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported. Methods: Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50-99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS - 0 -100 points) - previously developed and published - were measured. Results: 123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09-0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87-96), similar to the previously obtained value in the training dataset. Conclusion: the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.Open access funding provided by FCT|FCCN (b-on). Cardiovascular Center of the University of Lisbon, INESC-ID / Instituto Superior Técnico, University of Lisbon.Springer NatureRepositório da Universidade de LisboaMenezes, Miguel NobreSilva, João LourençoValente Silva, BeatrizRodrigues, TiagoGuerreiro, CláudioGuedes, João PedroSantos, Manuel OliveiraOliveira, Arlindo L.Pinto, Fausto J.2023-04-11T11:11:35Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/57068engInt J Cardiovasc Imaging. 2023 Apr 71569-579410.1007/s10554-023-02839-51875-8312info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:05:06Zoai:repositorio.ul.pt:10451/57068Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:07:31.071013Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model |
title |
Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model |
spellingShingle |
Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model Menezes, Miguel Nobre Artificial Intelligence Coronary angiography Coronary artery disease Deep learning Machine learning Percutaneous coronary intervention |
title_short |
Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model |
title_full |
Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model |
title_fullStr |
Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model |
title_full_unstemmed |
Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model |
title_sort |
Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model |
author |
Menezes, Miguel Nobre |
author_facet |
Menezes, Miguel Nobre Silva, João Lourenço Valente Silva, Beatriz Rodrigues, Tiago Guerreiro, Cláudio Guedes, João Pedro Santos, Manuel Oliveira Oliveira, Arlindo L. Pinto, Fausto J. |
author_role |
author |
author2 |
Silva, João Lourenço Valente Silva, Beatriz Rodrigues, Tiago Guerreiro, Cláudio Guedes, João Pedro Santos, Manuel Oliveira Oliveira, Arlindo L. Pinto, Fausto J. |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Menezes, Miguel Nobre Silva, João Lourenço Valente Silva, Beatriz Rodrigues, Tiago Guerreiro, Cláudio Guedes, João Pedro Santos, Manuel Oliveira Oliveira, Arlindo L. Pinto, Fausto J. |
dc.subject.por.fl_str_mv |
Artificial Intelligence Coronary angiography Coronary artery disease Deep learning Machine learning Percutaneous coronary intervention |
topic |
Artificial Intelligence Coronary angiography Coronary artery disease Deep learning Machine learning Percutaneous coronary intervention |
description |
© The Author(s) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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/. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04-11T11:11:35Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/57068 |
url |
http://hdl.handle.net/10451/57068 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Int J Cardiovasc Imaging. 2023 Apr 7 1569-5794 10.1007/s10554-023-02839-5 1875-8312 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Springer Nature |
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Springer Nature |
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