COVID-19 Lesion Segmentation Using Lung CT Scan Images: Comparative Study Based on Active Contour Models

Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation.

DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning

Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.

Association of Lung CT Findings in Coronavirus Disease 2019 (COVID-19) With Patients' Age, Body Weight, Vital Sings and Medical Regimen

COVID-19 spread quickly in Jordan in the past few months. Many changes have been observed in lungs of COVID-19 patients which required their hospitalization. So far, many studies have been conducted on the epidemiological features of COVID-19 illness; however, the evidence regarding the pathological influence on lungs is still lacking. Therefore, the main aim of the present study was to detect the possible association between lung computed tomography (CT) findings in COVID-19 and patients' age, body weight, vital sings, and medical regimen. The present cross-sectional study enrolled 230 COVID-19 patients in Prince Hamza Hospital in Jordan. Demographic data as well as major lung CT scan changes were obtained from the hospital records of the COVID-19 patients. The main observed major lung changes among the enrolled COVID-19 patients included ground glass opacification in 47(15.2%) patients and consolidation in 22(7.1%) patients. The higher percentage of patients with major lung changes (24%) was observed among patients above 60 years old, while (50%) of patients with no changes in their lung’s findings in the age group of 18-29 years old. Results obtained from the present study showed that only patients with major CT lung changes (9.7%) were prescribed more than three antibiotics. Additionally, 41.6 % patients with major lung CT scan changes had either dry (31.0%) or productive (10.6%) cough at the admission. Several predictors of lung CT scan changes have been detected in this study including age, BMI, medications, severity of symptoms, and cough at admission.

Continuous Lower Abdominal Compression as a Therapeutic Intervention in COVID-19 ARDS

We report the case of a patient with severe COVID-19 ARDS, suggesting a possible therapeutic intervention by applying a continuous lower abdominal compression. In order to assess ventilation distribution, a lung CT scan was performed with and without lower abdominal compression.