A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

PURPOSE: As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction. METHOD: We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction. RESULTS: For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort. CONCLUSION: The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.

Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.

A study on bone mass density using dual energy X-ray absorptiometry: Does high body mass index have protective effect on bone density in obese patients?

BACKGROUND: Osteoporosis is known as reduction of bone density, which is diagnosed using dual-energy X-ray absorptiometry. Although some studies have shown high body mass index (BMI) as a protective factor for osteoporosis and fracture risks, some other studies demonstrated obesity as a risk factor for osteoporosis. The aim of this study is to evaluate the relationship between BMI and bone mineral density (BMD) in premenopausal and postmenopausal females. Furthermore, we determined the correlation between BMI and fracture risk in postmenopausal females. MATERIALS AND METHODS: In this study, we evaluated the relationship between the age and BMI with 10-year probability fracture risk (estimated using fracture risk assessment tool) and BMD in the L1-L4 spine and femoral neck. Data were collected from BMD center, Askariye Hospital, Isfahan, Iran, from May 2016 to July 2017. RESULTS: The study consisted of 1361 individuals, including 305 premenopausal females and 1056 postmenopausal females. The results showed a statistically significant increase of BMD (P < 0.001) and a decrease of fracture risk (ß = -0.158, R 2 = 0.518) with an increase of BMI in postmenopausal females. Moreover, lumbar spine and femoral neck BMD were significantly higher in individuals with BMI ≥30 than in those with BMI <25 in both premenopausal and postmenopausal females (P < 0.001). In addition, older postmenopausal females indicated significantly lower L1-L4 BMD (r = -0.280, P < 0.05) and femoral neck BMD (r = -0.358, P < 0.05). CONCLUSION: The results showed a positive correlation between BMI and BMD of the spine and femoral neck which did not differ by menopausal status. However, there was a correlation between BMI and fracture risk in postmenopausal females.