Projects

COVID-19 pandemic has created extreme pressure on global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of the individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high sensitive C-reactive protein, and age - acquired at hospital admission were identified as key predictors of death by the multi-tree XGBoost model. The area under the curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate- and high-risk groups using LNLCA cut-off values of 10.4 and 12.65 with the death probability less than 5%, 5% to 50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in the early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification. 

We have developed an AI-enabled mobile application to record cough and breathing sounds for COVID-19 diagnosis. This application can be used as a pre-screening tool to decrease the pressure on health centers, provide a faster and more reliable testing mechanism to reduce the spread of the virus. 

QaTa-Cov19 is a joint project developed by researchers in Qatar University, Tampere University, and doctors from Hamad Medical Corporation for an accurate diagnosis, early detection, and infection map generation of Covid-19. The best deep AI model trained over the largest X-ray dataset with 119,316 images is now available for online trials below. This is a free application with no commercial strings attached. Simply Upload one or more X-ray images and you will see the infection maps generated in few seconds like the examples shown below.

QaTa-COV19 Dataset: The researchers of Qatar University and Tampere University have compiled QaTa-COV19 dataset, which consists of 4603 COVID-19 chest X-rays. From 4603 images, 2951 of them have their corresponding ground-truth segmentation masks, which can be found as mask_FILENAME.png.

Early-QaTa-COV19: This dataset is a subset of QaTa-COV19 dataset, which consists of 1065 chest X-rays including no or limited sign of COVID-19 pneumonia cases for early COVID-19 detection.

A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images. This COVID-19, normal, and other lung infection dataset is released in stages. In the first release, we have released 219 COVID-19, 1341 normal, and 1345 viral pneumonia chest X-ray (CXR) images. In the first update, we have increased the COVID-19 class to 1200 CXR images. In the 2nd update, we have increased the database to 3616 COVID-19 positive cases along with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients. 

A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Malaysia in collaboration with medical doctors from Hamad Medical Corporation and Bangladesh have created a database of chest X-ray images for Tuberculosis (TB) positive cases along with Normal images. In our current release, there are 3500 TB images, and 3500 normal images.