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In Medicina clinica (English ed.)

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.

Cobeñas Ricardo Luis, de Vedia María, Florez Juan, Jaramillo Daniela, Ferrari Luciana, Re Ricardo


Artificial intelligence, COVID-19, Lung, Machine learning, Pneumonia, Thoracic RX