Deep-stratification of the cardiovascular risk by ultrasound carotid artery images

Cardiovascular risk estimation functions predict the risk of cardiovascular events with clinical data and survivalmodels. These functions accurately stratify individuals into low, moderate, and high-risk categories. However,they tend to classify a considerable number of individuals into the middle-r...

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Autores: Grau, Maria, Gago, Lucas, Pérez Sánchez, Pablo, Remeseiro López, Beatriz, Igual Muñoz, Laura
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2024
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/224486
Acesso em linha:https://hdl.handle.net/2445/224486
Access Level:Acceso aberto
Palavra-chave:Aterosclerosi
Ecografia Doppler
Atherosclerosis
Doppler ultrasonography
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spelling Deep-stratification of the cardiovascular risk by ultrasound carotid artery imagesGrau, MariaGago, LucasPérez Sánchez, PabloGrau, MariaRemeseiro López, BeatrizIgual Muñoz, LauraAterosclerosiEcografia DopplerAtherosclerosisDoppler ultrasonographyCardiovascular risk estimation functions predict the risk of cardiovascular events with clinical data and survivalmodels. These functions accurately stratify individuals into low, moderate, and high-risk categories. However,they tend to classify a considerable number of individuals into the middle-risk category, and often, a subsequentreclassification into high-risk groups is required. Atherosclerosis is the leading cause of cardiovascular events,and ultrasound images of the Carotid Artery (CA) can detect its burden by measuring the carotid intimamediathickness and identifying atherosclerotic plaques. Current risk estimation functions do not considerultrasound imaging. This paper proposes the use of deep ultrasound CA image features in survival models toimprove risk stratification. In particular, we define new deep CA image features, extracting information froma convolutional neural network, and add them to an existing risk function. The experiments carried out showthat using deep image features improves the AUC of the risk function to 0.842, and these features are enoughto replace the information provided by blood biomarkers. Furthermore, the use of these features resulted in a20% improvement in the reclassification of risk categories, specifically for individuals who suffered an event,as shown by the net reclassification improvement metric.Elsevier2025202520242025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion10 p.application/pdfhttps://hdl.handle.net/2445/224486Articles publicats en revistes (Medicina)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1016/j.bspc.2024.106035Biomedical Signal Processing And Control, 2024, vol. 91https://doi.org/10.1016/j.bspc.2024.106035cc-by-nc-nd (c) Grau, Maria et al., 2024http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2244862026-05-29T05:05:01Z
dc.title.none.fl_str_mv Deep-stratification of the cardiovascular risk by ultrasound carotid artery images
title Deep-stratification of the cardiovascular risk by ultrasound carotid artery images
spellingShingle Deep-stratification of the cardiovascular risk by ultrasound carotid artery images
Grau, Maria
Aterosclerosi
Ecografia Doppler
Atherosclerosis
Doppler ultrasonography
title_short Deep-stratification of the cardiovascular risk by ultrasound carotid artery images
title_full Deep-stratification of the cardiovascular risk by ultrasound carotid artery images
title_fullStr Deep-stratification of the cardiovascular risk by ultrasound carotid artery images
title_full_unstemmed Deep-stratification of the cardiovascular risk by ultrasound carotid artery images
title_sort Deep-stratification of the cardiovascular risk by ultrasound carotid artery images
dc.creator.none.fl_str_mv Grau, Maria
Gago, Lucas
Pérez Sánchez, Pablo
Grau, Maria
Remeseiro López, Beatriz
Igual Muñoz, Laura
author Grau, Maria
author_facet Grau, Maria
Gago, Lucas
Pérez Sánchez, Pablo
Remeseiro López, Beatriz
Igual Muñoz, Laura
author_role author
author2 Gago, Lucas
Pérez Sánchez, Pablo
Remeseiro López, Beatriz
Igual Muñoz, Laura
author2_role author
author
author
author
dc.subject.none.fl_str_mv Aterosclerosi
Ecografia Doppler
Atherosclerosis
Doppler ultrasonography
topic Aterosclerosi
Ecografia Doppler
Atherosclerosis
Doppler ultrasonography
description Cardiovascular risk estimation functions predict the risk of cardiovascular events with clinical data and survivalmodels. These functions accurately stratify individuals into low, moderate, and high-risk categories. However,they tend to classify a considerable number of individuals into the middle-risk category, and often, a subsequentreclassification into high-risk groups is required. Atherosclerosis is the leading cause of cardiovascular events,and ultrasound images of the Carotid Artery (CA) can detect its burden by measuring the carotid intimamediathickness and identifying atherosclerotic plaques. Current risk estimation functions do not considerultrasound imaging. This paper proposes the use of deep ultrasound CA image features in survival models toimprove risk stratification. In particular, we define new deep CA image features, extracting information froma convolutional neural network, and add them to an existing risk function. The experiments carried out showthat using deep image features improves the AUC of the risk function to 0.842, and these features are enoughto replace the information provided by blood biomarkers. Furthermore, the use of these features resulted in a20% improvement in the reclassification of risk categories, specifically for individuals who suffered an event,as shown by the net reclassification improvement metric.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/224486
url https://hdl.handle.net/2445/224486
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.bspc.2024.106035
Biomedical Signal Processing And Control, 2024, vol. 91
https://doi.org/10.1016/j.bspc.2024.106035
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Grau, Maria et al., 2024
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc-nd (c) Grau, Maria et al., 2024
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Articles publicats en revistes (Medicina)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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