Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance

Objetivo Analizar la aplicación de inteligencia artificial (IA) en el diagnóstico y monitoreo de enfermedades respiratorias relacionadas al asbesto (ERRA), evaluando modelos de aprendizaje automático (ML) para mejorar la precisión diagnóstica y la toma de decisiones clínicas en poblaciones expuestas...

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Autores:
Gómez Ayarza, Víctor Aurelio
Tipo de recurso:
https://purl.org/coar/resource_type/c_7a1f
Fecha de publicación:
2025
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
spa
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/13938
Acceso en línea:
https://hdl.handle.net/20.500.12495/13938
Palabra clave:
Inteligencia artificial
Aprendizaje automático
Asbestosis
Mesotelioma maligno
Neoplasia pulmonar
Redes neurales de la computación
Biomarcadores de tumor
Radiografía torácica
Tomografía computarizada por rayos X
Minería de datos
Artificial intelligence
Machine learning
Asbestosis
Malignant mesothelioma
Lung neoplasm
Convolutional neural network
Tumor biomarkers
Thoracic radiography
X-ray computed tomography
Data mining
WA450
Rights
License
Attribution-NonCommercial-ShareAlike 4.0 International
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oai_identifier_str oai:repositorio.unbosque.edu.co:20.500.12495/13938
network_acronym_str UNBOSQUE2
network_name_str Repositorio U. El Bosque
repository_id_str
dc.title.none.fl_str_mv Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance
dc.title.translated.none.fl_str_mv Application of Artificial Intelligence in the diagnosis and monitoring of asbestos-related respiratory diseases: A scoping review
title Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance
spellingShingle Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance
Inteligencia artificial
Aprendizaje automático
Asbestosis
Mesotelioma maligno
Neoplasia pulmonar
Redes neurales de la computación
Biomarcadores de tumor
Radiografía torácica
Tomografía computarizada por rayos X
Minería de datos
Artificial intelligence
Machine learning
Asbestosis
Malignant mesothelioma
Lung neoplasm
Convolutional neural network
Tumor biomarkers
Thoracic radiography
X-ray computed tomography
Data mining
WA450
title_short Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance
title_full Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance
title_fullStr Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance
title_full_unstemmed Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance
title_sort Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcance
dc.creator.fl_str_mv Gómez Ayarza, Víctor Aurelio
dc.contributor.advisor.none.fl_str_mv Cetina Castillo, Lidy Yadira
Giraldo Luna, Clara Margarita
dc.contributor.author.none.fl_str_mv Gómez Ayarza, Víctor Aurelio
dc.contributor.orcid.none.fl_str_mv Gómez Ayarza, Víctor Aurelio [0009-0000-8947-7208]
dc.subject.none.fl_str_mv Inteligencia artificial
Aprendizaje automático
Asbestosis
Mesotelioma maligno
Neoplasia pulmonar
Redes neurales de la computación
Biomarcadores de tumor
Radiografía torácica
Tomografía computarizada por rayos X
Minería de datos
topic Inteligencia artificial
Aprendizaje automático
Asbestosis
Mesotelioma maligno
Neoplasia pulmonar
Redes neurales de la computación
Biomarcadores de tumor
Radiografía torácica
Tomografía computarizada por rayos X
Minería de datos
Artificial intelligence
Machine learning
Asbestosis
Malignant mesothelioma
Lung neoplasm
Convolutional neural network
Tumor biomarkers
Thoracic radiography
X-ray computed tomography
Data mining
WA450
dc.subject.keywords.none.fl_str_mv Artificial intelligence
Machine learning
Asbestosis
Malignant mesothelioma
Lung neoplasm
Convolutional neural network
Tumor biomarkers
Thoracic radiography
X-ray computed tomography
Data mining
dc.subject.nlm.none.fl_str_mv WA450
description Objetivo Analizar la aplicación de inteligencia artificial (IA) en el diagnóstico y monitoreo de enfermedades respiratorias relacionadas al asbesto (ERRA), evaluando modelos de aprendizaje automático (ML) para mejorar la precisión diagnóstica y la toma de decisiones clínicas en poblaciones expuestas. Metodología Se realizó una revisión de alcance con la metodología JBI y el protocolo PRISMA-ScR. La búsqueda en bases de datos científicas incluyó estudios entre 2019 y 2024 sobre IA aplicada al diagnóstico y monitoreo de ERRA. De 1095 estudios, se seleccionaron 40 relevantes tras aplicar filtros de inclusión, evaluando tecnologías, métricas y poblaciones. Resultados Los modelos de IA, incluyendo redes neuronales convolucionales (CNN, por sus siglas en inglés) y máquinas de soporte vectorial (SVM, por sus siglas en inglés), alcanzaron precisiones diagnósticas superiores al 90% en enfermedades como asbestosis y mesotelioma pleural. Herramientas como MesoNet y biomarcadores genéticos permitieron diagnósticos no invasivos y una mejor estratificación de riesgo en pacientes expuestos al asbesto. Conclusiones La IA ha demostrado su capacidad para optimizar el diagnóstico y monitoreo de ERRA. Sin embargo, persisten desafíos en la calidad de datos, generalización de modelos y equidad en el acceso. Para maximizar su impacto, es crucial el desarrollo de infraestructuras de datos accesibles, estándares de validación y colaboraciones interdisciplinarias que faciliten su integración en la práctica clínica.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-02-12T18:22:14Z
dc.date.available.none.fl_str_mv 2025-02-12T18:22:14Z
dc.date.issued.none.fl_str_mv 2025-01
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.local.spa.fl_str_mv Tesis/Trabajo de grado - Monografía - Especialización
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dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
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dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12495/13938
dc.identifier.instname.spa.fl_str_mv instname:Universidad El Bosque
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad El Bosque
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url https://hdl.handle.net/20.500.12495/13938
identifier_str_mv instname:Universidad El Bosque
reponame:Repositorio Institucional Universidad El Bosque
repourl:https://repositorio.unbosque.edu.co
dc.language.iso.fl_str_mv spa
language spa
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36. Devnath L, Luo S, Summons P, Wang D. Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs. Comput Biol Med [Internet]. 2021; 129:104125. https://doi.org/10.1016/j.compbiomed.2020.104125
37. Jurmeister P, Leitheiser M, Wolkenstein P, Klauschen F, Capper D, Brcic L. DNA methylation-based machine learning classification distinguishes pleural mesothelioma from chronic pleuritis, pleural carcinosis, and pleomorphic lung carcinomas. Lung Cancer [Internet]. 2022; 170:105–13. https://doi.org/10.1016/j.lungcan.2022.06.008
38. Cohen MW, Ghidotti A, Regazzoni D. Bi-level Analysis of Computed Tomography Images of Malignant Pleural Mesothelioma: Deep Learning-Based Classification and Subsequent Three-Dimensional Reconstruction. J Comput Inf Sci Eng. 2024;24(6). https://doi.org/10.1115/1.4064410
39. Yang HY. Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals. PLoS One. 2019;14(4). https://doi.org/10.1371/journal.pone.0214808
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spelling Cetina Castillo, Lidy YadiraGiraldo Luna, Clara MargaritaGómez Ayarza, Víctor AurelioGómez Ayarza, Víctor Aurelio [0009-0000-8947-7208]2025-02-12T18:22:14Z2025-02-12T18:22:14Z2025-01https://hdl.handle.net/20.500.12495/13938instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coObjetivo Analizar la aplicación de inteligencia artificial (IA) en el diagnóstico y monitoreo de enfermedades respiratorias relacionadas al asbesto (ERRA), evaluando modelos de aprendizaje automático (ML) para mejorar la precisión diagnóstica y la toma de decisiones clínicas en poblaciones expuestas. Metodología Se realizó una revisión de alcance con la metodología JBI y el protocolo PRISMA-ScR. La búsqueda en bases de datos científicas incluyó estudios entre 2019 y 2024 sobre IA aplicada al diagnóstico y monitoreo de ERRA. De 1095 estudios, se seleccionaron 40 relevantes tras aplicar filtros de inclusión, evaluando tecnologías, métricas y poblaciones. Resultados Los modelos de IA, incluyendo redes neuronales convolucionales (CNN, por sus siglas en inglés) y máquinas de soporte vectorial (SVM, por sus siglas en inglés), alcanzaron precisiones diagnósticas superiores al 90% en enfermedades como asbestosis y mesotelioma pleural. Herramientas como MesoNet y biomarcadores genéticos permitieron diagnósticos no invasivos y una mejor estratificación de riesgo en pacientes expuestos al asbesto. Conclusiones La IA ha demostrado su capacidad para optimizar el diagnóstico y monitoreo de ERRA. Sin embargo, persisten desafíos en la calidad de datos, generalización de modelos y equidad en el acceso. Para maximizar su impacto, es crucial el desarrollo de infraestructuras de datos accesibles, estándares de validación y colaboraciones interdisciplinarias que faciliten su integración en la práctica clínica.Especialista en Higiene IndustrialEspecializaciónObjective To analyze the application of artificial intelligence (AI) in the diagnosis and monitoring of asbestos-related respiratory diseases (ARRD), evaluating machine learning (ML) models to improve diagnostic accuracy and clinical decision-making in exposed populations. Methodology A scoping review was conducted using the JBI methodology and PRISMA-ScR protocol. The search in scientific databases included studies from 2019 to 2024 on AI applied to the diagnosis and monitoring of ARRD. Out of 1095 studies, 40 relevant ones were selected after applying inclusion filters, evaluating technologies, metrics, and populations. Results AI models, including convolutional neural networks (CNN) and support vector machines (SVM), achieved diagnostic accuracies above 90% for diseases such as asbestosis and pleural mesothelioma. Tools like MesoNet and genetic biomarkers enabled non-invasive diagnoses and improved risk stratification in asbestos-exposed patients. Conclusions AI has demonstrated its ability to optimize the diagnosis and monitoring of ARRD. However, challenges remain regarding data quality, model generalization, and equitable access. To maximize its impact, it is crucial to develop accessible data infrastructures, validation standards, and interdisciplinary collaborations to facilitate its integration into clinical practice.application/pdfAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/Acceso abiertohttps://purl.org/coar/access_right/c_abf2http://purl.org/coar/access_right/c_abf2Inteligencia artificialAprendizaje automáticoAsbestosisMesotelioma malignoNeoplasia pulmonarRedes neurales de la computaciónBiomarcadores de tumorRadiografía torácicaTomografía computarizada por rayos XMinería de datosArtificial intelligenceMachine learningAsbestosisMalignant mesotheliomaLung neoplasmConvolutional neural networkTumor biomarkersThoracic radiographyX-ray computed tomographyData miningWA450Aplicación de inteligencia artificial en el diagnóstico y monitoreo de enfermedades respiratorias asociadas al asbesto: una revisión de alcanceApplication of Artificial Intelligence in the diagnosis and monitoring of asbestos-related respiratory diseases: A scoping reviewEspecialización en Higiene IndustrialUniversidad El BosqueFacultad de MedicinaTesis/Trabajo de grado - Monografía - Especializaciónhttps://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesishttps://purl.org/coar/version/c_ab4af688f83e57aa1. 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