METHODS: COVID-19 causes lung infections, and qRT-PCR is an essential tool used to detect virus infection. However, qRT-PCR is inadequate for detecting the severity of the disease and the extent to which it affects the lung. In this paper, we aim to determine the severity level of COVID-19 by studying lung CT scans of people diagnosed with the virus.

RESULTS: We used images from King Abdullah University Hospital in Jordan; we collected our dataset from 875 cases with 2205 CT images. A radiologist classified the images into four levels of severity: normal, mild, moderate, and severe. We used various deep-learning algorithms to predict the severity of lung diseases. The results show that the best deep-learning algorithm used is Resnet101, with an accuracy score of 99.5% and a data loss rate of 0.03%.

METHODS: This retrospective cohort study was carried out at the Jordan University Hospital (JUH), a tertiary facility located in Amman, Jordan. Non-cystic Fibrosis Bronchiectasis (NCFB) was defined as an HRCT scan typical for bronchiectasis along with a negative sweat chloride test to rule out cystic fibrosis. Patients' data were collected by the use of Electronic Medical Records (EMR) at our institution. Frequent exacerbation was defined as more than 2 exacerbations in 1 year of the onset of the diagnosis.

RESULTS: A total of 79 patients were included, and 54.4% of them were female. The mean and standard deviation of the patient's age was 48.61 ± 19.62. The etiologies of bronchiectasis were evident in 79.7% of the sample. Asthma, Chronic Obstructive Pulmonary Diseases (COPD), and Kartagener syndrome were the most prevalent etiologies, accounting for related illnesses in 21.8%, 21.5%, and 13.9% of the patients, respectively. The most frequent bacteria cultured in our cohort were Pseudomonas and Candida Species. Moreover, 43 patients of the study cohort were frequent exacerbators, and 5 patients died.