Algorithmic determination of femoral neck axis in Birmingham hip resurfacing: A parametric CAD-based method
Abstract
Purpose
Birmingham hip resurfacing requires precise determination of the femoral head-neck axis to minimize risks of neck fracture, cortical notching, and component malposition. Existing methods rely on intraoperative estimation, generic guides, or semi-manual planning, all susceptible to anatomical variability. This study presents an automated parametric algorithm for determining the femoral neck axis from computed tomography (CT)-derived models to support patient-specific surgical planning.
Methods
The method utilizes segmented CT data converted to Standard for the Exchange of Product model data format. Following artifact removal, the algorithm performs iterative cross-sectional analysis along the neck axis, identifying characteristic anatomical points: the neck isthmus, head centroid, and head apex. The axis is approximated from these points and refined through rotational optimization in 5 degree and 0.5 degree increments. The algorithm was evaluated on 4 femoral models in 5 spatial orientations each (20 test cases).
Results
The algorithm achieved a mean validation score of 4.1/5.0 (82%). Three models with high-quality meshes scored 4–5, while one model with extensive artifacts scored 1–2, confirming sensitivity to input data quality. Repeatability was plus/minus 0.5 degrees in angular orientation and plus/minus 0.2 mm in isthmus position. Error propagation analysis indicated that CT-induced uncertainty (approximately 1.6 degrees upper bound) constitutes the limiting factor, not algorithmic precision. Physical prototyping confirmed viability for patient-specific instrument design.
Conclusion
The method provides reproducible, automated femoral neck axis determination with plus/minus 0.5 degrees repeatability – approximately three times smaller than CT-induced uncertainty and an order of magnitude below the 5–7 degree errors of conventional jigs. Future work should validate the method against clinical outcomes in larger cohorts.
© 2026 Michał P. Kowalik, Dawid Knaś, Krzysztof Zawiśliński, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.