Portable Detection and Characterization of the Synovium and Effusion inMusculoskeletal Ultrasound

Rheumatoid arthritis (RA) is an autoimmune disease often characterized by the inflammation of the synovium and effusion of joints. RA is a chronic condition and may affect quality of life. Detection and characterization of the synovium and effusion in joints is most often assessed by a trained sonographer or radiologist. Recently ultrasound has been brought into the clinic and used by other trained healthcare professionals such as clinicians and physiotherapists.

Ultrasound image analysis for identifying blood in an existing effusion in knee joint

Ultrasound, an inexpensive, accessible and portable device is gaining popularity in various disease diagnoses. In this project, we aim to analyze the ultrasound images generated for knee joint for diagnosing hemarthrosis (joint bleeding), a common clinical event in patients with severe hemophilia. We aim to analyze the images using machine learning and deep learning techniques.

Detection and Characterization of the Synovium in Musculoskeletal Ultrasound

Arthritis is a chronic disease that severely decreases the quality of life and affects almost 4.6 million Canadians, costing $33 billion for the Canadian economy every year. Affected individuals experience pain and disability through an extended period of time. Rheumatoid arthritis (RA), a common form of arthritis, is an autoimmune disease characterized by the inflammation of the synovium, or synovial membrane, a connective tissue that provides a cushion between bones and tendons and muscle around a joint.

Radiomic-based deep learning for time-to-event outcome in pulmonary malignancies.

Advances in medical image scanning technologies has allowed for great improvement in medical care, from early tumor detection, to trailered treatments and predicting treatment outcome. However, this has resulted in the generation of huge medical image databases, with thousands of medical image scans per patient that need to be examined by clinicians, making it impractical for clinicians to study all the images. This has led to the development of computer-assisted detection programs to automate part of this process.