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COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence

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dc.contributor.author Dorr, Francisco
dc.contributor.author Chaves, Hernán
dc.contributor.author Serra, María Mercedes
dc.contributor.author Ramirez, Andrés
dc.contributor.author Costa, Martín Elías
dc.contributor.author Seia, Joaquín
dc.contributor.author Cejas, Claudia Patricia
dc.contributor.author Castro, Marcelo
dc.contributor.author Eyheremendy, Eduardo
dc.contributor.author Fernández Slezak, Diego
dc.contributor.author Farez, Mauricio Franco
dc.contributor.author Study Collaborators
dc.date.accessioned 2021-04-19T11:53:02Z
dc.date.available 2021-04-19T11:53:02Z
dc.date.issued 2020-12
dc.identifier.citation Dorr, F., Chaves, H., Serra, M.M., Ramirez, A., Costa, M.E., Seia, J., Cejas, C., Castro, M., Eyheremendy, E., Fernández Slezak, D., Farez, M.F., 2020. COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence. Intell Based Med 3, 100014. https://doi.org/10.1016/j.ibmed.2020.100014 es_ES
dc.identifier.uri https://repositorio.fleni.org.ar/xmlui/handle/123456789/411
dc.identifier.uri https://doi.org/10.1016/j.ibmed.2020.100014
dc.description.abstract Purpose: To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support. Materials and methods: An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system. Results: Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p < 0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p < 0.001), although specificity decreased from 79% to 75% (p = 0.007). Conclusions: Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis. es_ES
dc.language.iso eng es_ES
dc.publisher Elsevier es_ES
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Artificial Intelligence es_ES
dc.subject Inteligencia Artificial es_ES
dc.subject Coronavirus Infections es_ES
dc.subject Infecciones por Coronavirus es_ES
dc.subject Radiography es_ES
dc.subject Radiografía es_ES
dc.title COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.type info:eu-repo/semantics/publishedVersion
dc.description.fil Fil: Farez, Mauricio Franco. Fleni. Centro para la Investigación de Enfermedades Neuroinmunológicas; Argentina. Entelai; Argentina.
dc.description.fil Fil: Chaves, Hernán. Fleni. Departamento de Diagnóstico por Imágenes; Argentina. Entelai; Argentina.
dc.description.fil Fil: Serra, María Mercedes. Fleni. Departamento de Diagnóstico por Imágenes; Argentina. Entelai; Argentina.
dc.description.fil Fil: Dorr, Francisco. Entelai; Argentina.
dc.description.fil Fil: Ramirez, Andrés. Entelai; Argentina.
dc.description.fil Fil: Costa, Martín Elías. Entelai; Argentina.
dc.description.fil Fil: Seia, Joaquín. Entelai; Argentina.
dc.description.fil Fil: Castro, Marcelo. Clínica Indisa. Department of Diagnostic Imaging; Chile.
dc.description.fil Fil: Eyheremendy, Eduardo. Hospital Alemán. Department of Diagnostic Imaging; Argentina.
dc.description.fil Fil: Fernández Slezak, Diego. Entelai; Argentina.Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Tecnológicas; Argentina.
dc.relation.ispartofVOLUME 3
dc.relation.ispartofPAGINATION 100014
dc.relation.ispartofCOUNTRY Paìses Bajos
dc.relation.ispartofCITY Amsterdam
dc.relation.ispartofTITLE Intelligence-based medicine
dc.relation.ispartofISSN 2666-5212
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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