ABSTRACT
This project outlines technical medical image processing and machine learning developments to study the
pathogenesis and natural history of osteoarthritis (OA). In the past few years, the availability of public datasets
that collect data such as plain radiographs, MRI genomics and patients reported outcomes has allowed the
study of disease etiology, potential treatment pathways and predictors of long-range outcomes, showing an
increasingly important role of the MRI. Moreover, recent advances in quantitative MRI and medical image
processing allow for the extraction of extraordinarily rich arrays of heterogeneous information on the
musculoskeletal system, including cartilage and bone morphology, bone shape features, biomechanics, and
cartilage biochemical composition.
Osteoarthritis, being a polygenic and multifactorial disease characterized by several phenotypes,
seems the perfect candidate for multidimensional analysis and precision medicine. However, accomplish this
ambitious task, will require complex analytics and multifactorial data-integration from diverse assessments
spanning morphological, biochemical, and biomechanical features. In this project, we propose to fill this gap
developing automatic post-processing algorithms to examine cartilage biochemical compositional and
morphological features and to apply new multidimensional machine learning to study OA
This “Pathway to Independence” award application includes a mentored career development plan to
transition the candidate, Dr. Valentina Pedoia, into an independent investigator position, as well as an
accompanying research plan describing the proposed technical developments for the application of big data
analytics to the study of OA. The primary mentor, Dr. Sharmila Majumdar, is a leading expert in the field of
quantitative MRI for the study of OA, and the co-mentors, Dr. Adam Ferguson and Dr. Ramakrishna Akella,
have extensive experience in the application of machine learning and topological data analysis to big data. The
diversified plan of training and the complementary background of these mentors will allow the candidate to
develop a unique interdisciplinary profile in the field of musculoskeletal imaging.
The candidate, Dr. Valentina Pedoia, is currently in a post-doctoral level position (Associated
Specialist) at the University of California at San Francisco (UCSF), developing MR image post-processing
algorithms. The mentoring and career development plan will supplement her image processing background
with valuable exposure to machine learning, big data analysis, epidemiological study design, and
interdisciplinary collaboration to facilitate her transition to a medical imaging and data scientist independent
investigator position. Ultimately, she aims to become a faculty member in a radiology or bioengineering
institute, where she can further research technical biomedical imaging and machine learning developments
applied to the musculoskeletal system.