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

MARIA MERCEDES SERRA; JOAQUIN SEIA; ROSA CASTAGNA; HERNAN CHAVES; MARTIN ELIAS COSTA; PABLO KUSCHNER; FRANCISCO DORR ; ANDRES RAMIREZ; CLAUDIA CEJAS; CLAUDIO GUTIERREZ OCHIUZZI

INTELLIGENCE-BASED MEDICINE

ELSEVIER

Lugar: CABA; Año: 2020 vol. 3 p. 1 - 7

2666-5212

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 vali-dation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AURO Coverall (70% increase in sensitivity and 1% increase in specificity, p