Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial

Autores
Curiale, Ariel Hernán; Calandrelli, Matías Enrique; Dellazoppa, Lucca; Trevisan, Mariano; Bocián, Jorge Luis; Bonifacio, Juan Pablo; Mato, German
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantifica- tion of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.
Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.
Fil: Curiale, Ariel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Harvard Medical School; Estados Unidos
Fil: Calandrelli, Matías Enrique. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina
Fil: Dellazoppa, Lucca. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Universidad Nacional de Cuyo; Argentina
Fil: Trevisan, Mariano. Provincia de Río Negro. Sanatorio San Carlos; Argentina
Fil: Bocián, Jorge Luis. Provincia de Río Negro. Sanatorio San Carlos; Argentina
Fil: Bonifacio, Juan Pablo. Provincia de Río Negro. Sanatorio San Carlos; Argentina
Fil: Mato, German. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Universidad Nacional de Cuyo; Argentina. Provincia de Río Negro. Sanatorio San Carlos; Argentina
Materia
Deep Learning
Heart Diseases
Diagnostic Imaging
Magnetic Resonance Imaging
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/157647
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/157647
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificialAutomatic Quantification of Volumes and Biventricular Function in Cardiac Resonance: Validation of a New Artificial Intelligence ApproachCuriale, Ariel HernánCalandrelli, Matías EnriqueDellazoppa, LuccaTrevisan, MarianoBocián, Jorge LuisBonifacio, Juan PabloMato, GermanDeep LearningHeart DiseasesDiagnostic ImagingMagnetic Resonance Imaginghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantifica- tion of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.Fil: Curiale, Ariel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Harvard Medical School; Estados UnidosFil: Calandrelli, Matías Enrique. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; ArgentinaFil: Dellazoppa, Lucca. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Universidad Nacional de Cuyo; ArgentinaFil: Trevisan, Mariano. Provincia de Río Negro. Sanatorio San Carlos; ArgentinaFil: Bocián, Jorge Luis. Provincia de Río Negro. Sanatorio San Carlos; ArgentinaFil: Bonifacio, Juan Pablo. Provincia de Río Negro. Sanatorio San Carlos; ArgentinaFil: Mato, German. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Universidad Nacional de Cuyo; Argentina. Provincia de Río Negro. Sanatorio San Carlos; ArgentinaSociedad Argentina de Cardiología2021-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/157647Curiale, Ariel Hernán; Calandrelli, Matías Enrique; Dellazoppa, Lucca; Trevisan, Mariano; Bocián, Jorge Luis; et al.; Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial; Sociedad Argentina de Cardiología; Revista Argentina de Cardiología; 89; 4; 10-2021; 1-50034-70001850-3748CONICET DigitalCONICETenghttp://www.old2.sac.org.ar/wp-content/uploads/2021/10/v89n4a12s.pdfinfo:eu-repo/semantics/altIdentifier/url/http://www.old2.sac.org.ar/revista-argentina-de-cardiologia/?texto=Cuantificaci%C3%B3n+autom%C3%A1tica&autor=&secciones=tipoDeSecci%C3%B3n&periodo=info:eu-repo/semantics/altIdentifier/doi/10.7775/rac.es.v89.i4.20427info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:44:05Zoai:ri.conicet.gov.ar:11336/157647instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:44:05.72CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial
Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance: Validation of a New Artificial Intelligence Approach
title Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial
spellingShingle Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial
Curiale, Ariel Hernán
Deep Learning
Heart Diseases
Diagnostic Imaging
Magnetic Resonance Imaging
title_short Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial
title_full Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial
title_fullStr Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial
title_full_unstemmed Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial
title_sort Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial
dc.creator.none.fl_str_mv Curiale, Ariel Hernán
Calandrelli, Matías Enrique
Dellazoppa, Lucca
Trevisan, Mariano
Bocián, Jorge Luis
Bonifacio, Juan Pablo
Mato, German
author Curiale, Ariel Hernán
author_facet Curiale, Ariel Hernán
Calandrelli, Matías Enrique
Dellazoppa, Lucca
Trevisan, Mariano
Bocián, Jorge Luis
Bonifacio, Juan Pablo
Mato, German
author_role author
author2 Calandrelli, Matías Enrique
Dellazoppa, Lucca
Trevisan, Mariano
Bocián, Jorge Luis
Bonifacio, Juan Pablo
Mato, German
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Deep Learning
Heart Diseases
Diagnostic Imaging
Magnetic Resonance Imaging
topic Deep Learning
Heart Diseases
Diagnostic Imaging
Magnetic Resonance Imaging
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantifica- tion of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.
Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.
Fil: Curiale, Ariel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Harvard Medical School; Estados Unidos
Fil: Calandrelli, Matías Enrique. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina
Fil: Dellazoppa, Lucca. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Universidad Nacional de Cuyo; Argentina
Fil: Trevisan, Mariano. Provincia de Río Negro. Sanatorio San Carlos; Argentina
Fil: Bocián, Jorge Luis. Provincia de Río Negro. Sanatorio San Carlos; Argentina
Fil: Bonifacio, Juan Pablo. Provincia de Río Negro. Sanatorio San Carlos; Argentina
Fil: Mato, German. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Universidad Nacional de Cuyo; Argentina. Provincia de Río Negro. Sanatorio San Carlos; Argentina
description Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantifica- tion of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/157647
Curiale, Ariel Hernán; Calandrelli, Matías Enrique; Dellazoppa, Lucca; Trevisan, Mariano; Bocián, Jorge Luis; et al.; Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial; Sociedad Argentina de Cardiología; Revista Argentina de Cardiología; 89; 4; 10-2021; 1-5
0034-7000
1850-3748
CONICET Digital
CONICET
url http://hdl.handle.net/11336/157647
identifier_str_mv Curiale, Ariel Hernán; Calandrelli, Matías Enrique; Dellazoppa, Lucca; Trevisan, Mariano; Bocián, Jorge Luis; et al.; Cuantificación automática de los volúmenes y función de ambos ventrículos en resonancia cardíaca: Propuesta y evaluación de un método de inteligencia artificial; Sociedad Argentina de Cardiología; Revista Argentina de Cardiología; 89; 4; 10-2021; 1-5
0034-7000
1850-3748
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.old2.sac.org.ar/wp-content/uploads/2021/10/v89n4a12s.pdf
info:eu-repo/semantics/altIdentifier/url/http://www.old2.sac.org.ar/revista-argentina-de-cardiologia/?texto=Cuantificaci%C3%B3n+autom%C3%A1tica&autor=&secciones=tipoDeSecci%C3%B3n&periodo=
info:eu-repo/semantics/altIdentifier/doi/10.7775/rac.es.v89.i4.20427
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedad Argentina de Cardiología
publisher.none.fl_str_mv Sociedad Argentina de Cardiología
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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