Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis

Deep Venous Thrombosis (DVT) is a manifestation of a Thromboembolic Disease (ET). When in a DVT the venous thrombus detaches and travel through the bloodstream can cause a Pulmonary Embolism Thrombus (PET). The existence of Deep Venous Thrombosis (DVT) in the lower extremities has been described as...

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Autores principales:	Fong-Mata, Maria Berenice, Inzunza-González, Everardo, García-Guerrero, Enrique Efrén, Mejía Medina, David Abdel, Morales Contreras, Oscar Adrián, Gómez-Roa, Antonio
Formato:	info:eu-repo/semantics/article
Lenguaje:	spa
Publicado:	Universidad Autónoma de Baja California 2020
Materias:	Diagnosis Artificial neural networks Deep venous thrombosis. Diagnóstico Redes neuronales artificiales Trombosis venosa profunda.
Acceso en línea:	https://recit.uabc.mx/index.php/revista/article/view/2oba15
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	repositorioinstitucional-20.500.12930-6970
record_format	dspace
institution	Repositorio Institucional
collection	DSpace
language	spa
topic	Diagnosis Artificial neural networks Deep venous thrombosis. Diagnóstico Redes neuronales artificiales Trombosis venosa profunda.
spellingShingle	Diagnosis Artificial neural networks Deep venous thrombosis. Diagnóstico Redes neuronales artificiales Trombosis venosa profunda. Fong-Mata, Maria Berenice Inzunza-González, Everardo García-Guerrero, Enrique Efrén Mejía Medina, David Abdel Morales Contreras, Oscar Adrián Gómez-Roa, Antonio Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
description	Deep Venous Thrombosis (DVT) is a manifestation of a Thromboembolic Disease (ET). When in a DVT the venous thrombus detaches and travel through the bloodstream can cause a Pulmonary Embolism Thrombus (PET). The existence of Deep Venous Thrombosis (DVT) in the lower extremities has been described as one of the main risk factors for the development of PET. It is considered that up to 90% of pulmonary emboli come from venous thrombi of the lower extremities. The most commonly used techniques for the detection of DVT are clinical probability models, D-dimer and non-invasive imaging tests, such as ultrasound for DVT and computed angiotomography (CT) for pulmonary embolism. However, due to the non-specificity of the symptoms of DVT, the threshold for ordering an ultrasound is low, in addition to being a complicated process that requires the participation of a specialist doctor for its interpretation. In recent decades, machine learning has emerged as support in decision-making for the diagnosis of various diseases, some of the most used technologies in the field of medicine include Support Vector Machine (SVM), Decision Trees and Neural Networks Artificial (RNA). This article reviews the existing technologies for the detection of DVT as well as the main machine learning algorithms commonly used in biomedical applications; The design of a computerized system that uses machine learning techniques as a support tool for the timely detection of a possible DVT is proposed.
format	info:eu-repo/semantics/article
author	Fong-Mata, Maria Berenice Inzunza-González, Everardo García-Guerrero, Enrique Efrén Mejía Medina, David Abdel Morales Contreras, Oscar Adrián Gómez-Roa, Antonio
author_facet	Fong-Mata, Maria Berenice Inzunza-González, Everardo García-Guerrero, Enrique Efrén Mejía Medina, David Abdel Morales Contreras, Oscar Adrián Gómez-Roa, Antonio
author_sort	Fong-Mata, Maria Berenice
title	Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_short	Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_full	Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_fullStr	Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_full_unstemmed	Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_sort	deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
publisher	Universidad Autónoma de Baja California
publishDate	2020
url	https://recit.uabc.mx/index.php/revista/article/view/2oba15
_version_	1792609339418083328
spelling	repositorioinstitucional-20.500.12930-69702023-05-09T14:35:07Z Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis Trombosis venosa profunda en extremidades inferiores: revisión de las técnicas de diagnóstico actuales y su simbiosis con el aprendizaje automático para un diagnóstico oportuno Fong-Mata, Maria Berenice Inzunza-González, Everardo García-Guerrero, Enrique Efrén Mejía Medina, David Abdel Morales Contreras, Oscar Adrián Gómez-Roa, Antonio Diagnosis Artificial neural networks Deep venous thrombosis. Diagnóstico Redes neuronales artificiales Trombosis venosa profunda. Deep Venous Thrombosis (DVT) is a manifestation of a Thromboembolic Disease (ET). When in a DVT the venous thrombus detaches and travel through the bloodstream can cause a Pulmonary Embolism Thrombus (PET). The existence of Deep Venous Thrombosis (DVT) in the lower extremities has been described as one of the main risk factors for the development of PET. It is considered that up to 90% of pulmonary emboli come from venous thrombi of the lower extremities. The most commonly used techniques for the detection of DVT are clinical probability models, D-dimer and non-invasive imaging tests, such as ultrasound for DVT and computed angiotomography (CT) for pulmonary embolism. However, due to the non-specificity of the symptoms of DVT, the threshold for ordering an ultrasound is low, in addition to being a complicated process that requires the participation of a specialist doctor for its interpretation. In recent decades, machine learning has emerged as support in decision-making for the diagnosis of various diseases, some of the most used technologies in the field of medicine include Support Vector Machine (SVM), Decision Trees and Neural Networks Artificial (RNA). This article reviews the existing technologies for the detection of DVT as well as the main machine learning algorithms commonly used in biomedical applications; The design of a computerized system that uses machine learning techniques as a support tool for the timely detection of a possible DVT is proposed. La Trombosis Venosa Profunda (TVP) es una manifestación de una Enfermedad Tromboembólica (ET). Cuando en una TVP los trombos venosos se desprenden y viajan a través del torrente sanguíneo pueden ocasionar una Trombo Embolia Pulmonar (TEP). La existencia de Trombosis Venosa Profunda (TVP) en las extremidades inferiores se ha descrito como uno de los principales factores de riesgo para el desarrollo de la TEP. Se considera que hasta el 90% de los émbolos pulmonares proceden de trombos venosos de las extremidades inferiores. Las técnicas más utilizadas para la detección de TVP son los modelos de probabilidad clínica, el dímero D y las pruebas de imagen no invasivas, como la ecografía para la TVP y la angiotomografía computadorizada (TC) para el embolismo pulmonar. Sin embargo, debido a la inespecificidad de los síntomas de la TVP, el umbral para ordenar una ecografía es bajo, además de ser un proceso complicado que requiere la participación de un médico especialista para su interpretación. En las últimas décadas el aprendizaje automático ha surgido como apoyo en la toma de decisiones para el diagnóstico de diversas enfermedades, algunas de las tecnologías más utilizadas en el campo de la medicina incluyen Support Vector Machine (SVM), Árboles de decisión y las Redes Neuronales Artificiales (RNA). En el presente artículo se hace una revisión de las tecnologías existentes para la detección de la TVP así como de los principales algoritmos de aprendizaje automático comúnmente utilizados en aplicaciones biomédicas; se propone el diseño de un sistema computarizado que utilice técnicas de aprendizaje automático como herramienta de apoyo para la detección oportuna de un posible padecimiento de TVP. 2020-07-15 2021-06-03T03:52:15Z 2021-06-03T03:52:15Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://recit.uabc.mx/index.php/revista/article/view/2oba15 10.37636/recit.v312334 https://hdl.handle.net/20.500.12930/6970 spa https://recit.uabc.mx/index.php/revista/article/view/2oba15/19 https://recit.uabc.mx/index.php/revista/article/view/2oba15/html https://recit.uabc.mx/index.php/revista/article/view/2oba15/2OBA15 Copyright (c) 2020 Fong-Mata María Berenice, Inzunza-González Everardo, García-Guerrero Enrique Efrén, Mejía Medina David Abdel, Morales Contreras Oscar Adrián, Gómez-Roa Antonio http://creativecommons.org/licenses/by/4.0 application/pdf text/html application/xml Universidad Autónoma de Baja California REVISTA DE CIENCIAS TECNOLÓGICAS; Vol. 3 No. 1 (2020); 23-34 REVISTA DE CIENCIAS TECNOLÓGICAS; Vol. 3 Núm. 1 (2020); 23-34 2594-1925

Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis

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