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

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.

Disposable Isolation Device to Reduce COVID-19 Contamination During CT Scanning.

RATIONALE AND OBJECTIVES: The use of chest computed tomography (CT) in the era of the COVID-19 pandemic raises concern regarding the transmission risks to patients and staff caused by CT room contamination. Meanwhile the Center for Disease Control guidance for air exchange in between patients may heavily impact workflows. To design a portable custom isolation device to reduce imaging equipment contamination during a pandemic. MATERIALS AND METHODS: Center for Disease Control air exchange guidelines and requirements were reviewed. Device functional requirements were outlined and designed. Engineering requirements were reviewed. Methods of practice and risk mitigation plans were outlined including donning and doffing procedures and failure modes. Cost impact was assessed in terms of CT patient throughput. RESULTS: CT air exchange solutions and alternatives were reviewed. Multiple isolation bag device designs were considered. Several designs were custom fabricated, prototyped and reduced to practice. A final design was tested on volunteers for comfort, test-fit, air seal, and breathability. Less than 14 times enhanced patient throughput was estimated, in an ideal setting, which could more than counterbalance the cost of the device itself. CONCLUSION: A novel isolation bag device is feasible for use in CT and might facilitate containment and reduce contamination in radiology departments during the COVID Pandemic.