Osteoarthritis (OA) is a prevalent musculoskeletal disease, and radiographic assessment remains the standard for diagnosis and grading. However, expert grading is subjective and intensity-based automated methods are sensitive to imaging variability. As a potential solution to these problems, landmark-based approaches are worth exploring. Landmark-based representations of bone geometry offer an alternative to pixel-based inputs, reducing sensitivity to imaging artifacts and emphasizing structural variation. This thesis compares four landmark encodings (raw x,y coordinates, Procrustes-aligned points, pairwise distances, and polar coordinates) and evaluates them using both linear dimensionality reduction (PCA) and nonlinear generative modeling (VAEs) on hip radiographs from a publicly available dataset. We evaluate reconstruction fidelity, latent space traversal, correlation with clinical outcomes, and classification performance. Results show that raw point coordinates provide a strong baseline, often matching or outperforming more complex encodings in classification, while alternative representations improved interpretability but not discriminative power. PCA preserved clinically meaningful variability, whereas VAEs underperformed in this unsupervised setting. These findings suggest that landmark annotations already contain sufficient information for supervised OA tasks, while more advanced models may be needed for unsupervised or generative applications.