Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images

PURPOSEChest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.METHODSA retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student’s t-test or Mann-Whitney U test. Cohen’s kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.RESULTSFifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (p = 0.008) and comorbidities (p = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (p = 0.0008 and p = 0.049) or central line (p = 0.003 and p = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.CONCLUSIONChest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.

The imaging features of novel coronavirus (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) pandemic are still being fully characterized and understood (1, 2). The estimated mortality rate is reported between 1.4% and 7% (3). Health services and intensive care units (ICUs) are facing critical saturation in this pandemic (4), where early and wise resource allocation decisions may impact population outcomes.Radiology departments play a key role in this pandemic (5–8), with imaging data potentially contributing towards detection (9–14), characterization (9), monitoring (15–18), triage (19–22), resource allocation, early intervention, and isolation (8). Although speculative, models that correlate imaging findings to outcomes could be helpful or predictive in the management and triage of the 20% of SARS-CoV-2 positive patients who develop more serious manifestations of COVID-19 pneumonia. Epidemiology standards require a waiting period in between patients with airborne viral diseases, which may practically limit computed tomography (CT) use. To date, radiology and thoracic professional societies have pointed to the efficiency, ease of access, field availability, and repeatability of chest X-ray as well as its ease of cleaning and decontamination. These strengths are balanced against the higher sensitivity and specificity of CT. Moreover, when patients are encouraged to present early in the course of their disease, as was the case in Hubei Province, China, chest X-ray may have less value than CT.Typical and characteristic CT features for COVID-19 related pneumonia have been recently defined (23–28). Chest X-ray findings might help address clinical decision-making in screening, management and prioritization that may unfortunately arise in the care of COVID-19 patients. Resource allocation may be most critical during peak prevalence, when imaging equipment may also be stretched thin, or not accessible to intensive care or “medical surge facility” settings.Deep learning uses convolutional neural networks that are like a “black box” in that they may or may not use conventional imaging features to function and classify the outputs. Machine learning on the other hand, would use specific features, and generally requires less data points to ensure clinically relevant accuracy or validity. This study uses tools for explanatory purposes, not for producing a refined or usable model at this early stage. There are currently limited reports of the role of chest X-ray in COVID-19 patients with scarce details of the application and role of deep learning of chest X-rays in patients with COVID-19. Previous papers stated that the main findings were bilateral reticular nodular opacities, ground-glass opacities and peripheral consolidations (29–31). Deep learning and artificial intelligence (AI) applications in chest radiography are in their infancy, but there are multiple commercial platforms for computer-aided detection for pulmonary nodule detection, characterization and quantification of interstitial lung disease (32–34). We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.