An artificial intelligence framework for universal landmark matching and morphometry in musculoskeletal radiography

OBJECTIVE: Accurate morphometric measurements are crucial for musculoskeletal radiography, but they remain labor-intensive and prone to inter-reader variability. Current artificial intelligence-based solutions often require large annotated training datasets and narrow applications. We present and validate a training-free artificial intelligence framework that automatically derives morphometric measurements across multiple anatomies and radiographic views using universal landmark matching. MATERIALS AND METHODS: In this retrospective study, 600 standard radiographs of the foot, knee, and shoulder are analyzed. Additionally, a cohort of 240 challenging radiographs containing orthopedic implants was constructed to stress-test the approach. Landmarks from reference radiographs are transferred to unseen radiographs using a pre-trained generalist dense-matching method, and are then used to derive measurements in a post-processing step. The resulting measurements were compared with manual annotations and measurements by two radiologists. RESULTS: Mean landmark matching error is 2.68 ± 2.70 mm using a single reference radiograph and improves to 2.15 ± 2.38 mm with 40 reference radiographs. Measurement accuracy ranges from 1.81° (I-II metatarsal angle) to 8.65° (congruence angle). Increasing the number of reference images improved measurement accuracy, and mostly approached inter-reader agreement. Performance is mixed on the challenging cohort, demonstrating the limitations and strengths of the approach. CONCLUSIONS: This anatomy-agnostic framework enables training-free morphometry across multiple regions, with measurement-dependent performance often comparable to inter-reader agreement. Challenging cases highlight specific limitations, motivating the use of quality control and reference-set tuning for deployment. Its minimal setup enables rapid adaptation to new anatomies and measurements, and clinically practical runtimes require GPU inference. KEY POINTS: Question Can a generalist artificial intelligence framework be used to accurately and automatically perform morphometric measurements across different musculoskeletal radiographs without anatomy-specific training? Findings The training-free approach achieved performance that approaches expert-level agreement for most measurements, while highlighting measurement-specific limitations in challenging cases. Multiple reference radiographs improved results. Clinical relevance This approach automates repetitive morphometric measurements that are prone to inter-reader variability, reducing manual workload while providing reproducible results that can approach expert radiologist performance. Its adaptability and minimal setup enable integration into routine workflows.