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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/157647
Ver los metadatos del registro completo
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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 Técnicas2025-09-03T09:49:49Zoai: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:34982025-09-03 09:49:49.993CONICET 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 https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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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1842268997014257664 |
score |
13.13397 |