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Medicina clinica (English ed.)
Jan 20, 2023;160 (2): 78-81.

Diagnostic performance of artificial intelligence algorithms for detection of pulmonary involvement by COVID-19 based on portable radiography.

Ricardo Luis Cobeñas, María de Vedia, Juan Florez, Daniela Jaramillo, Luciana Ferrari, Ricardo Re
Author Information
  1. Ricardo Luis Cobeñas: Departamento de Diagnóstico por Imágenes, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno (CEMIC), Buenos Aires, Argentina.
  2. María de Vedia: Departamento de Diagnóstico por Imágenes, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno (CEMIC), Buenos Aires, Argentina.
  3. Juan Florez: Departamento de Diagnóstico por Imágenes, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno (CEMIC), Buenos Aires, Argentina.
  4. Daniela Jaramillo: Departamento de Diagnóstico por Imágenes, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno (CEMIC), Buenos Aires, Argentina.
  5. Luciana Ferrari: Departamento de Diagnóstico por Imágenes, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno (CEMIC), Buenos Aires, Argentina.
  6. Ricardo Re: Departamento de Diagnóstico por Imágenes, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno (CEMIC), Buenos Aires, Argentina.
PMID: 36597473 DOI: 10.1016/j.medcle.2022.04.020

Abstract

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.

Keywords

Artificial intelligence COVID-19 Lung Machine learning Pneumonia Thoracic RX

References

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