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Rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por COVID-19 basados en radiografía portátil

INTRODUCTION AND OBJECTIVES: To evaluate the diagnostic performance of different artificial intelligence (AI) algorithms for the identification of pulmonary involvement by SARS-CoV-2 based on portable chest radiography (RX). MATERIAL AND METHODS: Prospective observational study that included patient...

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Autores principales: Cobeñas, Ricardo Luis, de Vedia, María, Florez, Juan, Jaramillo, Daniela, Ferrari, Luciana, Re, Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier España, S.L.U. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283603/
https://www.ncbi.nlm.nih.gov/pubmed/35918213
http://dx.doi.org/10.1016/j.medcli.2022.04.016
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author Cobeñas, Ricardo Luis
de Vedia, María
Florez, Juan
Jaramillo, Daniela
Ferrari, Luciana
Re, Ricardo
author_facet Cobeñas, Ricardo Luis
de Vedia, María
Florez, Juan
Jaramillo, Daniela
Ferrari, Luciana
Re, Ricardo
author_sort Cobeñas, Ricardo Luis
collection PubMed
description INTRODUCTION AND OBJECTIVES: To evaluate the diagnostic performance of different artificial intelligence (AI) algorithms for the identification of pulmonary involvement by SARS-CoV-2 based on portable chest radiography (RX). MATERIAL AND METHODS: Prospective observational study that included patients admitted for suspected COVID-19 infection in a university hospital between July and November 2020. The reference standard of pulmonary involvement by SARS-CoV-2 comprised a positive PCR test and low-tract respiratory symptoms. RESULTS: 493 patients were included, 140 (28%) with positive PCR and 32 (7%) with SARS-CoV-2 pneumonia. The AI-B algorithm had the best diagnostic performance (areas under the ROC curve AI-B 0.73, vs. AI-A 0.51, vs. AI-C 0.57). Using a detection threshold greater than 55%, AI-B had greater diagnostic performance than the specialist [(area under the curve of 0.68 (95% CI 0.64-0.72), vs. 0.54 (95% CI 0.49-0.59)]. CONCLUSION: AI algorithms based on portable RX enabled a diagnostic performance comparable to human assessment for the detection of SARS-CoV-2 lung involvement.
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spelling pubmed-92836032022-07-15 Rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por COVID-19 basados en radiografía portátil Cobeñas, Ricardo Luis de Vedia, María Florez, Juan Jaramillo, Daniela Ferrari, Luciana Re, Ricardo Med Clin (Barc) Original Breve INTRODUCTION AND OBJECTIVES: To evaluate the diagnostic performance of different artificial intelligence (AI) algorithms for the identification of pulmonary involvement by SARS-CoV-2 based on portable chest radiography (RX). MATERIAL AND METHODS: Prospective observational study that included patients admitted for suspected COVID-19 infection in a university hospital between July and November 2020. The reference standard of pulmonary involvement by SARS-CoV-2 comprised a positive PCR test and low-tract respiratory symptoms. RESULTS: 493 patients were included, 140 (28%) with positive PCR and 32 (7%) with SARS-CoV-2 pneumonia. The AI-B algorithm had the best diagnostic performance (areas under the ROC curve AI-B 0.73, vs. AI-A 0.51, vs. AI-C 0.57). Using a detection threshold greater than 55%, AI-B had greater diagnostic performance than the specialist [(area under the curve of 0.68 (95% CI 0.64-0.72), vs. 0.54 (95% CI 0.49-0.59)]. CONCLUSION: AI algorithms based on portable RX enabled a diagnostic performance comparable to human assessment for the detection of SARS-CoV-2 lung involvement. Elsevier España, S.L.U. 2023-01-20 2022-07-15 /pmc/articles/PMC9283603/ /pubmed/35918213 http://dx.doi.org/10.1016/j.medcli.2022.04.016 Text en © 2022 Elsevier España, S.L.U. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Original Breve
Cobeñas, Ricardo Luis
de Vedia, María
Florez, Juan
Jaramillo, Daniela
Ferrari, Luciana
Re, Ricardo
Rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por COVID-19 basados en radiografía portátil
title Rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por COVID-19 basados en radiografía portátil
title_full Rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por COVID-19 basados en radiografía portátil
title_fullStr Rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por COVID-19 basados en radiografía portátil
title_full_unstemmed Rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por COVID-19 basados en radiografía portátil
title_short Rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por COVID-19 basados en radiografía portátil
title_sort rendimiento diagnóstico de algoritmos de inteligencia artificial para detección de compromiso pulmonar por covid-19 basados en radiografía portátil
topic Original Breve
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283603/
https://www.ncbi.nlm.nih.gov/pubmed/35918213
http://dx.doi.org/10.1016/j.medcli.2022.04.016
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