Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs

Abstract

This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1±14.9yo; 29F 60.1±14.3yo; 4 missing information). Three expert chest radiologists scored the left and right lung separately based on the degree of opacity (0-3) and geographic extent (0-4). Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. Data were split into 80% training and 20% testing datasets. Correlation analysis between AI-predicted versus radiologist scores were analyzed. Comparison was made with traditional and transfer learning. The average opacity score was 2.52 (range: 0-6) with a standard deviation of 0.25 (9.9%) across three readers. The average geographic extent score was 3.42 (range: 0-8) with a standard deviation of 0.57 (16.7%) across three readers. The inter-rater agreement yielded a Fleiss' Kappa of 0.45 for opacity score and 0.71 for extent score. AI-predicted scores strongly correlated with radiologist scores, with the top model yielding a correlation coefficient (R 2) of 0.90 (range: 0.73-0.90 for traditional learning and 0.83-0.90 for transfer learning) and a mean absolute error of 8.5% (ranges: 17.2-21.0% and 8.5%-15.5, respectively). Transfer learning generally performed better. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This approach may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation.

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks

“…Salehinejad et al used a deep convolutional generative adversarial network (DCGAN) to overcome the imbalanced radiographs dataset [13]. Nowadays, deep learning algorithms are widely used to analyze chest radiographs for COVID-19 diagnosis [14][15][16]. Zhu et al employed CNNs to stage the lung disease severity of COVID-19 infection on portable chest radiographs [14].…”

“…Nowadays, deep learning algorithms are widely used to analyze chest radiographs for COVID-19 diagnosis [14][15][16]. Zhu et al employed CNNs to stage the lung disease severity of COVID-19 infection on portable chest radiographs [14]. Oh et al proposed a patch-based CNN, to deal with a small COVID-19 radiograph dataset [15].…”

Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs

“…AUC and accuracy were not reported. AI has also been employed to stage pCXR disease severity against radiologist scores Zhu et al, 2020a). Our study had one of the larger cohorts, balanced sample sizes, and multi-class model.…”

“…Many AI algorithms based on deep-learning convolutional neural networks have been deployed for pCXR applications (Harris et al, 2019;Heo et al, 2019;Mekov, Miravitlles & Petkov, 2020) and these algorithms can be readily repurposed for COVID-19 pandemic circumstances. While there are already many papers describing prevalence and radiographic features on pCXR of COVID-19 lung infection (see reviews (Bao et al, 2020)), there are a few AI papers (Apostolopoulos & Mpesiana, 2020;Elaziz et al, 2020;Hurt, Kligerman & Hsiao, 2020;Murphy et al, 2020;Ozturk et al, 2020;Pereira et al, 2020;Zhu et al, 2020a) to classify CXRs of COVID-19 patients from CXR of normals or related lung infections. The full potential of AI applications of pCXR under COVID-19 pandemic circumstances is not yet fully realized.…”

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“…In their guidelines, the following observations were given for COVID-19 diagnosis. ( i )) X-rays was found to be lower in sensitivity and higher specificity than chest CT imaging [ 188 ] [ 189 ]. (ii) Chest X-rays are less expensive, have lower radiation, takes less acquisition time, and are less expensive to use for monitoring than CT. (iii) Chest CT was found to have higher sensitivity but lower specificity and emits more radiation than X-ray.…”