Background: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods: We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results: At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions: This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.
Castiglioni, I., Ippolito, D., Interlenghi, M., Monti, C., Salvatore, C., Schiaffino, S., et al. (2021). Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy. EUROPEAN RADIOLOGY EXPERIMENTAL, 5(1 (December 2021)) [10.1186/s41747-020-00203-z].
|Citazione:||Castiglioni, I., Ippolito, D., Interlenghi, M., Monti, C., Salvatore, C., Schiaffino, S., et al. (2021). Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy. EUROPEAN RADIOLOGY EXPERIMENTAL, 5(1 (December 2021)) [10.1186/s41747-020-00203-z].|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy|
|Autori:||Castiglioni, I; Ippolito, D; Interlenghi, M; Monti, C; Salvatore, C; Schiaffino, S; Polidori, A; Gandola, D; Messa, C; Sardanelli, F|
SALVATORE, CHRISTIAN (Corresponding)
|Data di pubblicazione:||2021|
|Rivista:||EUROPEAN RADIOLOGY EXPERIMENTAL|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1186/s41747-020-00203-z|
|Appare nelle tipologie:||01 - Articolo su rivista|