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. 2021 Mar 16;11(3):530.
doi: 10.3390/diagnostics11030530.

Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia

Christian Salvatore  1   2 , Matteo Interlenghi  2 , Caterina B Monti  3 , Davide Ippolito  4 , Davide Capra  3 , Andrea Cozzi  3 , Simone Schiaffino  5 , Annalisa Polidori  2 , Davide Gandola  4 , Marco Alì  6 , Isabella Castiglioni  7   8 , Cristina Messa  9   10 , Francesco Sardanelli  3   5
Affiliations

Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia

Christian Salvatore et al. Diagnostics (Basel). .

Abstract

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

Keywords: COVID-19; SARS-CoV-2; artificial intelligence; chest X-ray; community-acquired pneumonia; differential diagnosis; neural networks; sensitivity; specificity.

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Conflict of interest statement

Christian Salvatore declares to be CEO of DeepTrace Technologies SRL, a spin-off of Scuola Universitaria Superiore IUSS, Pavia, Italy. Matteo Interlenghi, Annalisa Polidori, and Isabella Castiglioni declare to own DeepTrace Technologies S.R.L shares. Simone Schiaffino declares to have received travel support from Bracco Imaging and to be a member of the speakers’ bureau for General Electric Healthcare. Marco Alì declares to be a scientific advisor for Bracco Imaging. Francesco Sardanelli declares to have received grants from, or to be a member of, the speakers’ bureau/advisory board for Bayer Healthcare, the Bracco Group, and General Electric Healthcare. All other authors have nothing to disclose.

Figures

Figure 1
Figure 1
Chest X-ray images of subjects with (a) COVID-19 pneumonia, (b) negative examination, (c) viral pneumonia, and (d) bacterial pneumonia.
Figure 2
Figure 2
(Left) areas under the curve at receiver operating characteristic analysis for COVID-19 versus negative and (Right) for COVID-19 versus community-acquired pneumonia in the cross-validation phase.
See this image and copyright information in PMC

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