Automated diagnosis of liver fibrosis and steatosis using deep-learning algorithms applied to conventional liver ultrasound

Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver disorders worldwide. NAFLD could lead to end-stage liver disease and considered as one of the most common causes of liver transplantation. Moreover, a large number of liver transplant recipients develop NAFLD after transplantation due to side effects of the medications that they have to use to keep their new liver healthy. Although liver biopsy has been used for a long time to evaluate the liver condition it is associated with a risk of bleeding and other side effects. Consequently, we aim to improve simple liver ultrasound method by adding some artificial intelligence algorithms to the images obtained from this method to diagnose NAFLD and other liver disorders after transplantation. By doing so, clinicians will be able to perform real-time liver ultrasound without the need to refer the patients to a radiologist for the procedure.

Faculty Supervisor:

Mamatha Bhat


Amirhossein Azhie






University of Toronto



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