Knee osteoarthritis (OA) is a global problem that causes joint pain and decreased mobility and quality of life. Knee OA costs the Canadian economy billions of dollars. Cartilage and bone are both implicated in knee OA pathogenesis. Obesity is a major risk factor for knee OA. Physical activity decreases pain and improves quality of life in those with knee OA. Nonetheless, we have limited biomechanical evidence to create concrete recommendations for prescription of aerobic exercise that improves clinical outcomes without exacerbating pain or worsening joint structures in knee OA. We have a limited understanding of how cartilage of the OA knee responds to physical activity, and the role of bone shape on the response.
This thesis fills four identified gaps in the literature. First, Chapter 2 used a fully-crossed random assignment study design where 40 healthy participants completed 18 bicycling positions to define novel equations for setting bicycle saddle position based on minimum or maximum knee flexion angle. This work is important because the current gold-standard of setting bicycle saddle position for mitigating injury focuses on a desired knee flexion angle; yet no easy methods exist. Second, Chapter 3 used the same dataset to identify how joint kinematics affect tibiofemoral and patellofemoral joint forces during bicycling. This work showed joint forces are least sensitive to the gold standard bicycle-fit parameter, minimum knee flexion angle; instead, minimum hip flexion angle was the most important. Third, Chapter 4 describes and validates a multi-stage convolutional neural network framework for efficiently segmenting cartilage and bone from magnetic resonance imaging data. The algorithm produced state-of-the-art predictions on the commonly tested Osteoarthritis Initiative dataset in an average of 1.5 mins per knee. These methods will be crucial for improving experimental and epidemiologic studies of cartilage and bone. Fourth, Chapter 5 combines statistical shape models of the tibia and femur, joint forces estimated at the knee, and statistical parametric mapping to explore continuously over the cartilage surface how cartilage deforms after walking and bicycling. This study showed for the first time that the acute response of cartilage in women with symptomatic knee OA is dependent on bone shape and knee joint forces.
The bicycle-fit related studies provide the first comprehensive insights into how lower limb joint kinematics affect knee joint forces and provide novel equations to use this knowledge to easily set bicycle saddle position in the clinic, bicycle shop, or at home. The image analysis chapter describes an image segmentation framework that excels when applied to the knee. The final chapter integrates biomechanical measures with statistical shape models using custom data processing pipelines that yielded new insights and that hold great potential for evoking novel and specific findings about knee OA pathophysiology at the intersection of bone, cartilage, and mechanics. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26084 |
| Date | January 2020 |
| Creators | Gatti, Anthony A |
| Contributors | Maly, Monica R, Rehabilitation Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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