Xin Gao

Xin Gao

R3T researchers

Chair and Professor, Computer Science Program; Interim Director, Computational Bioscience Research Center (CBRC); Deputy Director, Smart Health Initiative (SHI)

Research Interests

CT-based detection, segmentation and classification system for COVID-19

Xin Gao's group has been developing an AI-based computer-aided diagnosis (CAD) system for detection of COVID-19 patients (especially the early stage); classification of the disease phase; segmentation of the infection; and quantification of the infection regions. The experience from clinicians battling COVID-19 all over the world has shown that purely relying on pathogen nucleic acid or antibody detection is not fully reliable and can result in missed detections, which has become a big threat to the global community. Therefore, CT-imaging, as a sensitive and readily accessible biomedical imaging technology, has been routinely used as one of the main diagnostic standards, in addition to nucleic acid detection. 

The CT images from different stages of patients' illnesses have very different patterns. Patients at the middle and advanced phases often have symptoms already, and their CT images are easily identified by radiologists. However, the early phase very often requires a high level of expert knowledge and experience to differentiate. In fact, CT images of the early phase of COVID-19 patients can look very similar to those of other lung infection patients, such as RSV pneumonia and carbon monoxide poisoning. Furthermore, quantifying the area for infection from the CT images has been shown to be very important for patient prognosis and treatment. Gao's goal is to help Saudi clinicians and physicians to more efficiently analyze and diagnose the patients and provide them guidelines to proper treatment.