Active multi-disciplinary research is ongoing to discover quantitative biomarkers for early diagnosis, monitoring and assessment of joint degeneration. Medical imaging has played a substantial role in this area. Advanced quantitative imaging techniques, novel computerized image post-processing and more recent machine learning (ML) techniques have made possible further advances towards quantitative characterization of early joint degeneration and identification of imaging biomarkers associated with OA. Deep learning advances are revolutionizing the use of imaging in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times and processing of MRI, conducting large-scale longitudinal studies, and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. In this talk, I’ll explore how recent applications of DL have improved imaging-based understanding of knee OA. I’ll illustrate how DL techniques are applied at all stages of imaging to enable automation of acquisition analysis and new imaging biomarkers discovery.
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