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

Abstract

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.

COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system

“…Indeed, radiographic findings of COVID-19 pneumonia include bilateral ground-glass opacities and consolidation [ 8 – 10 , 39 ], which has substantial overlap with pneumonia from other etiologies. Although it is difficult to directly compare the performance across different studies because of difference in test datasets, recent studies where deep learning-based CADs that were specifically trained for COVID-19 pneumonia reported higher AUCs compared to our results (AUCs, 0.81–0.99, Table 5 ) for the identification of CXRs from COVID-19 patients [ 27 , 28 , 40 , 41 ]. Additional training of the CAD with COVID-19 CXRs may improve the performance.…”

Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers

“…A total of 20 studies that met these criteria were assessed for the risk of bias with the QUADAS-2 tool. [5] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] Eight studies with a high risk of bias rating in at least two domains were excluded ( figure 1 , appendix, Table 3. ).…”

Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans

“…The outcomes of this study achieved an accuracy of higher than 71%, specificity of higher than 71%, and sensitivity of higher than 77%. Another study was conducted by Sekeroglu and Ozsahin from Turkey [15] who employed machine learning as well as deep learning techniques in detecting chest x-ray images for patients. This study includes 6100 images (Healthy:1583, Pneumonia:4292, and COVID-19:225).…”