Introduction. The objective assessment of shoulder function is important for personalized diagnosis, therapies and evidence-based practice but has been limited by specialized equipment and dedicated movement laboratories. Advances in AI-driven computer vision (CV) using consumer RGB cameras (red-blue-green) and open-source CV models offer the potential for routine clinical use. However, key concepts, evidence, and research gaps have not yet been synthesized to drive clinical translation. This scoping review aims to map related literature. Method. Following the JBI Manual for Evidence Synthesis, a scoping review was conducted on PubMed and Scholar using search terms including “shoulder,” “pose estimation,” “camera”, and others. From 146 initial results, 27 papers focusing on clinical applicability and using consumer cameras were included. Analysis employed a Grounded Theory approach guided iterative refinement. Result. Studies primarily used Microsoft Kinect (infrared-based depth sensing, RGB camera; discontinued) or monocular consumer cameras with open-source CV-models, sometimes supplemented by LiDAR (laser-based depth sensing), wearables or markers. Technical validation studies against gold standards were scarce and too inconsistent for comparison. Larger range of motion (RoM) movements were accurately recorded, but smaller movements, rotations and scapula tracking remained challenging. For instance, one larger validation study comparing shoulder angles during arm raises to a marker-based gold-standard reported Pearson's R = 0.98 and a standard error of 2.4deg. OpenPose and Mediapipe were the most used CV-models. Recent efforts try to improve model performance by training with shoulder specific movements. Conclusion. Low-cost, routine clinical movement analysis to assess shoulder function using consumer cameras and CV seems feasible. It can provide acceptable accuracy for certain movement tasks and larger RoM. Capturing small, hidden or the entirety of shoulder movement requires improvements such as via training models with shoulder specific data or using dual cameras. Technical validation studies require methodological standardization, and clinical validation against established constructs is needed for translation into practice

Trauma and orthopaedics is the largest of the surgical specialties and yet attracts a disproportionately small fraction of available national and international funding for health research. With the burden of musculoskeletal disease increasing, high-quality research is required to improve the evidence base for orthopaedic practice. Using the current research landscape in the United Kingdom as an example, but also addressing the international perspective, we highlight the issues surrounding poor levels of research funding in trauma and orthopaedics and indicate avenues for improving the impact and success of surgical musculoskeletal research.

Cite this article: Bone Joint J 2014; 96-B:1578–85.