Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

• Published in: European radiology experimental 2021-02, Vol.5 (1), p.7-7, Article 7
• Main Authors: Castiglioni, Isabella, Ippolito, Davide, Interlenghi, Matteo, Monti, Caterina Beatrice, Salvatore, Christian, Schiaffino, Simone, Polidori, Annalisa, Gandola, Davide, Messa, Cristina, Sardanelli, Francesco
• Format: Article
• Language: English
• Subjects: Aged; Artificial intelligence; Coronaviruses; COVID-19; Deep learning; Diagnostic Radiology; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Italy; Lung - diagnostic imaging; Machine Learning; Male; Medical diagnosis; Medicine; Medicine & Public Health; Middle Aged; Neural networks (computer); Neuroradiology; Original; Original Article; Radiographic Image Interpretation, Computer-Assisted - methods; Radiography, Thoracic - methods; Radiology; Reproducibility of Results; Retrospective Studies; SARS-CoV-2; Sensitivity and specificity; Ultrasound; X-Rays
• ISSN: 2509-9280; 2509-9280
• DOI: 10.1186/s41747-020-00203-z

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.