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

Abstract

Purpose: Chest 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.

Methods: A 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.

Results: Fifty-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.

Conclusion: Chest 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.

Figure 1. a, b

Chest X-ray and clinical data correlation for COVID-19 positive patients. Panel (a) shows comparison of patients with (n=37) and without (n=11) the presence of either alveolar or interstitial opacity. Alveolar or interstitial opacity were statistically correlated to a higher age and higher number of comorbidities. Panel (b) shows the distribution of patients with and without the presence of alveolar or interstitial opacity. This data suggests a trend of increasing alveolar and interstitial opacity with age and comorbidities.

Figure 2

Correlation of endotracheal tube and central line placement with either alveolar or interstitial opacities severity. Comparison of severity of parenchymal opacities on baseline X-ray for patients with (n=6) and without (n=42) endotracheal tube (ETT) or with (n=9) and without (n=39) central line placement. The p values indicated.

Figure 3. a, b

Longitudinal chest X-ray evaluation. Panel (a) shows the change in alveolar opacity severity versus days since first scan. Changes in imaging findings and if a central line was placed are indicated at corresponding timepoints. Panel (b) shows longitudinal chest X-ray series for an 81-year-old female patient, whose disease course is documented in panel (a) (dark green line).

Figure 4. a–d

Illustrative case of deep learning model for lung segmentation and classification of alveolar and interstitial opacities. Baseline chest X-ray (a) of a patient with both alveolar and interstitial opacities. The total lung field segmentation image (b) of the same patient. Note that retro-cardiac left lower lobe is erroneously neglected from total lung field segmentation, a challenging problem with frontal chest X-ray lung field segmentation. Alveolar opacity heat map (c). Interstitial opacity heat map (d). Note that the map used the retrocardiac region, but did not use the region of the small round ground glass opacity in the mid left lung (arrow).