Ph.D., Assistant Professor, Radiology and Biomedical Imaging, UCSF
Biography
Dr. Pedoia 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. Her current research focus is on exploring the role of machine learning in the extraction of contributors to musculoskeletal disorders as 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 relevant 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.
Session Abstract – PMWC 2020 Silicon Valley
Track 2 - January 24 1.45 P.M.-2.30 P.M.
One of the most promising areas of health innovation is the application of artificial intelligence, machine learning, and deep learning primarily in medical imaging. The increase in the amount of data and the possibility to use ML/DL to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to more objective science. Radiologists, who were at the forefront of the digital era in medicine, can guide the introduction of AI solutions that accommodate ethical and regulatory requirements into healthcare.
