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.

Salvatore, C., Interlenghi, M., Monti, C., Ippolito, D., Capra, D., Cozzi, A., et al. (2021). Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia. DIAGNOSTICS, 11(3) [10.3390/diagnostics11030530].

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

Salvatore, Christian
;
Ippolito, Davide;Gandola, Davide;Castiglioni, Isabella
;
Messa, Cristina;
2021

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.
Articolo in rivista - Articolo scientifico
Artificial intelligence; Chest X-ray; Community-acquired pneumonia; COVID-19; Differential diagnosis; Neural networks; SARS-CoV-2; Sensitivity; Specificity;
English
2021
Salvatore, C., Interlenghi, M., Monti, C., Ippolito, D., Capra, D., Cozzi, A., et al. (2021). Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia. DIAGNOSTICS, 11(3) [10.3390/diagnostics11030530].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/310865
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