Automated MRI-based assessment of paediatric femoral geometry: establishing objective reference values for femoral neck–shaft and anteversion angles

Abstract

Introduction & Purpose

Femoral neck–shaft angle (NSA) and femoral anteversion angle (AVA) influence lower-limb

alignment, joint loading, and movement biomechanics during growth (Figure 1a). Altered

femoral geometry has been associated with changes in gait mechanics and musculoskeletal

overload, highlighting its relevance for clinical biomechanics, sports medicine, and paediatric

movement research (Kainz et al., 2023). Although femoral morphology has been widely

investigated (Szuper et al., 2015; Staheli et al., 1985; Bobroff et al., 1999), large-scale

assessment remains challenging due to time-consuming manual measurements, operator

dependency, and methodological variability (Boese et al., 2016; Kaiser et al., 2016). This study

aimed to develop an automated MRI-based workflow combining deep learning femur

segmentation with deterministic geometric analysis to quantify NSA and AVA and generate

reference distributions in typically developing children.

Methods

Whole-limb MRI scans of 52 typically developing children, 24 female and 28 male, without

known lower-limb pathologies or pain, aged 5–16 years, were analysed. In total, 104 femora

were available. Femora for which automated geometric fitting did not yield plausible angle

estimates were excluded. Femoral segmentation was performed using a self-trained 3D nnU-

Net model (Isensee et al., 2021) trained on 37 manually annotated femur segmentations

(Figure 1b). The resulting masks were processed using the deterministic MSKsInertiaGeom

pipeline, combining STAPLE-based processing of segmented bone geometries, following

Modenese and Renault (2021), with inertia-axis calculations and refined geometric fitting to

derive NSA and AVA without manual landmark placement. Linear mixed-effects models

accounted for bilateral femoral measurements, with participant identity as a random effect and

age, sex, and age-by-sex interaction as fixed effects. Significance was defined as p < 0.05.

Results

Automated geometric fitting succeeded in 102 of 104 femora. Mean NSA was 132.9 ± 5.8° with

a range of 120° to 145°, while mean AVA was 22.4 ± 10.0° with a range of 0° to 45°. Linear

mixed-effects modelling demonstrated a significant negative association between age and

NSA, with β = −0.94°/year and p < 0.001. AVA showed no statistically significant association

with age, with β = −0.70°/year and p = 0.109. No statistically significant sex-related differences

were observed for NSA or AVA, and no significant age-by-sex interactions were detected. The

resulting distributions provide reference values across the investigated age range (Figure 1c).

Discussion

This automated MRI-based framework enables scalable, operator-independent quantification

of paediatric femoral morphology. The observed age-related decrease in NSA is consistent

with reported developmental patterns of proximal femoral morphology (Hefti, 2007), whereas

AVA demonstrated substantial inter-individual variability and no significant age dependency.

Limitations include the single-centre and cross-sectional design, lack of formal validation

against independent manual segmentation and measurement protocols and failed geometric

fitting in two femora.

Conclusion

An automated MRI-based pipeline integrating deep learning segmentation and deterministic

geometric analysis enables objective quantification of paediatric femoral NSA and AVA. These

reference values may support future biomechanical and sports science research investigating

femoral morphology in movement development, physical activity, sports participation, and

musculoskeletal overload during growth.

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