She is a data scientist with a primary interest in developing algorithms for advanced computer vision and machine learning for improving the usage of non-invasive imaging as diagnostic and prognostic tools.
Dr. Pedoia obtained her doctoral degree in computer science working on features extraction from functional and structural brain MRI in subjects with glial tumors. After graduation, in 2013, she joined the Musculoskeletal and Imaging Research Group at UCSF as post-doctoral fellow. Her role was in providing support and expertise in medical computer vision, with a focus to reduce human effort and to extract semantic features from MRI to study degenerative joint disease.
Her current main research focus is on exploring the role of machine learning in the extraction of contributors to osteoarthritis (OA). She is studying analytics to model the complex interactions between morphological, biochemical and biomechanical aspects of the knee joint as a whole; deep learning convolutional neural network for musculoskeletal tissue segmentation and for the extraction of silent features from quantitative relaxation maps for a comprehensive study of the biochemical articular cartilage composition; with ultimate goal of developing a completely data-driven model that is able to extract imaging features and use them to identify risk factors and predict outcomes.