Title:
Re-Examine Musculoskeletal Disease by Deep Learning
Re-Examine Musculoskeletal Disease by Deep Learning
Time:
01/08 (Sat.) 8 pm PST, 9 pm MST, 10 pm CST, 11 pm EST
01/09 (Sun.) 5 am CET, 12 pm Taiwan
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01/08 (Sat.) 8 pm PST, 9 pm MST, 10 pm CST, 11 pm EST
01/09 (Sun.) 5 am CET, 12 pm Taiwan
Time zone conversion tool
Keywords:
Medical Images, Deep Learning, Osteoarthritis, MRI, Deep generative Models
Medical Images, Deep Learning, Osteoarthritis, MRI, Deep generative Models
為配合本研究發表時程，本次錄影將延遲上傳
Abstract:
The management of degenerative and age-related diseases are expected to be one of the prominent challenges to Taiwan’s medical system as the population ages. Osteoarthritis (OA) is one of the most prevalent and debilitating degenerative and age-related diseases, with its burden expected to increase by several folds around the globe due to population aging and risks factors such as increasing rates of obesity. Deep learning-based imaging biomarkers provide great promise in specifying novel treatments target, facilitating new strategies of early detection and prevention techniques, and identifying population that is vulnerable to disease progression and disability. It is therefore of great importance to explore these new opportunities to alleviate the burden of OA and other age-related musculoskeletal diseases, especially in an aging society of Taiwan. In this talk, we will cover our efforts to study morphological biomarkers of early OA and onset of OA by performing automatic knee MRI segmentation across various population and development of the explainable AI models to identify potential target of treatment of reducing osteoarthritis (OA) pain and minimizing likelihood of surgical intervention.
The management of degenerative and age-related diseases are expected to be one of the prominent challenges to Taiwan’s medical system as the population ages. Osteoarthritis (OA) is one of the most prevalent and debilitating degenerative and age-related diseases, with its burden expected to increase by several folds around the globe due to population aging and risks factors such as increasing rates of obesity. Deep learning-based imaging biomarkers provide great promise in specifying novel treatments target, facilitating new strategies of early detection and prevention techniques, and identifying population that is vulnerable to disease progression and disability. It is therefore of great importance to explore these new opportunities to alleviate the burden of OA and other age-related musculoskeletal diseases, especially in an aging society of Taiwan. In this talk, we will cover our efforts to study morphological biomarkers of early OA and onset of OA by performing automatic knee MRI segmentation across various population and development of the explainable AI models to identify potential target of treatment of reducing osteoarthritis (OA) pain and minimizing likelihood of surgical intervention.
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