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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier España, S.L.U.
2023
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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. |
format | Online Article Text |
id | pubmed-9283603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier España, S.L.U. |
record_format | MEDLINE/PubMed |
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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