Rethinking femoral neck anteversion assessment: a novel automated 3D CT method compared to traditional manual techniques.
Authors
Affiliations (4)
Affiliations (4)
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China.
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
- Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China. [email protected].
- Beijing Rossum Robot Technology Co., Ltd, Beijing, China.
Abstract
To evaluate the accuracy and reliability of a novel automated 3D CT-based method for measuring femoral neck anteversion (FNA) compared to three traditional manual methods. A total of 126 femurs from 63 full-length CT scans (35 men and 28 women; average age: 52.0 ± 14.7 years) were analyzed. The automated method used a deep learning network for femur segmentation, landmark identification, and anteversion calculation, with results generated based on two axes: Auto_GT (using the greater trochanter-to-intercondylar notch center axis) and Auto_P (using the piriformis fossa-to-intercondylar notch center axis). These results were validated through manual landmark annotation. The same dataset was assessed using three conventional manual methods: Murphy, Reikeras, and Lee methods. Intra- and inter-observer reliability were assessed using intraclass correlation coefficients (ICCs), and pairwise comparisons analyzed correlations and differences between methods. The automated methods produced consistent FNA measurements (Auto_GT: 17.59 ± 9.16° vs. Auto_P: 17.37 ± 9.17° on the right; 15.08 ± 9.88° vs. 14.84 ± 9.90° on the left). Intra-observer ICCs ranged from 0.864 to 0.961, and inter-observer ICCs between Auto_GT and the manual methods were high, except for the Lee method. No significant differences were observed between the two automated methods or between the automated and manual verification methods. Moreover, strong correlations (R > 0.9, p < 0.001) were found between Auto_GT and the manual methods. The novel automated 3D CT-based method demonstrates strong reproducibility and reliability for measuring femoral neck anteversion, with performance comparable to traditional manual techniques. These results indicate its potential utility for preoperative planning, postoperative evaluation, and computer-assisted orthopedic procedures. Not applicable.