Valentina Pedoia is a specialist in the Musculoskeletal and Imaging Research Group at UCSF and a data scientist focusing on developing algorithms for advanced computer vision and machine learning for improving the usage of noninvasive imaging as diagnostic and prognostic tools. Her current research explores the role of machine learning in the extraction of contributors to osteoarthritis (OA), and she is studying analytics to model the complex interactions between morphological, biochemical, and biomechanical aspects of the knee joint as a whole and 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 the 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. Previously, she was a postdoc in the Musculoskeletal and Imaging Research Group, where she provided support and expertise in medical computer vision with a focus on reducing human effort and extracting semantic features from MRIs to study degenerative joint disease. Valentina’s recent work on machine learning applied to OA was awarded as annual scientific highlights of the 25th conference of the International Society of Magnetic Resonance In Medicine (ISMRM 2017) and selected as best paper presented at the MRI drug discovery study group. Valentina holds a PhD in computer science, where her research focused on feature extraction from functional and structural brain MRI in subjects with glial tumors.
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