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

Maxime Blain, Michael T. Kassin, Nicole Varble, Xiaosong Wang, Ziyue Xu, Daguang Xu, Gianpaolo Carrafiello, Valentina Vespro, Elvira Stellato, Anna Maria Ierardi, Letizia Di Meglio, Robert D. Suh, Stephanie A. Walker, Sheng Xu, Thomas H. Sanford, Evrim B. Turkbey, Stephanie Harmon, Baris Turkbey, Bradford J. Wood

Research output: Contribution to journalArticlepeer-review


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 auto-mated 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. Co-hen’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 opac-ities 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 interpre-tation, 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.

Original languageEnglish
Pages (from-to)20-27
Number of pages8
JournalDiagnostic and Interventional Radiology
Issue number1
Publication statusPublished - 2021

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine


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