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Objective <p>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.</p> Materials and methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis><i> Can a generalist artificial intelligence framework be used to accurately and automatically perform morphometric measurements across different musculoskeletal radiographs without anatomy-specific training?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis><i> 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</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis><i> 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</i>.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Dennis Eschweiler,
  • Eneko Cornejo Merodio,
  • Felix Barajas Ordonez,
  • Aleksandar Lichev,
  • Nikol Ignatova,
  • Marc Sebastian von der Stück,
  • Christiane K. Kuhl,
  • Daniel Truhn,
  • Sven Nebelung

摘要

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.

Graphical Abstract