A Clinical Primer on Computer Vision

Abstract Computer vision is a rapidly evolving field within computer science that focuses on extracting meaningful information from digital images. In medical imaging, computer vision techniques such as object detection, classification, and segmentation are essential for analysing complex anatomical structures. This primer provides an overview of computer vision concepts and methods, highlighting both traditional approaches and modern machine learning techniques. Landmark detection and shape modelling are also discussed as these are particularly relevant for musculoskeletal imaging. Challenges associated with applying computer vision in clinical practice are examined. This includes considerations such as data limitations, domain shift, and the need for robust validation. Finally, future directions for integrating computer vision into osteoporosis research and clinical workflows are explored, emphasising the importance of explainability, fairness, and adherence to guiding principles for trustworthy clinical tools.