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Predicting the hip joint centre in children: New regression equations, linear scaling, and statistical shape
Laura Carman1, Thor F Besier2, Julie Choisne1
1Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Journal of Biomechanics
|August 26, 2022
Summary
Estimating the hip joint centre (HJC) is crucial for biomechanics. A paediatric statistical shape model offers the most accurate HJC estimation without medical imaging, outperforming regression equations and bone scaling methods.
Area of Science:
- Biomechanics
- Paediatric Orthopaedics
- Medical Imaging Analysis
Background:
- Accurate hip joint centre (HJC) determination is vital for analyzing hip joint motion, moments, and muscle forces.
- Non-invasive HJC estimation methods are of significant interest, particularly for paediatric populations where data is limited.
- Previous evaluations of HJC estimation techniques have not extensively focused on the paediatric demographic.
Purpose of the Study:
- To evaluate and compare the accuracy of different methods for estimating the hip joint centre (HJC) in a paediatric population.
- To identify the most precise non-invasive method for HJC determination in children and adolescents.
- To develop and validate new regression equations for paediatric HJC estimation.
Main Methods:
- Utilized 3D pelvis models segmented from 333 CT scans of children aged 4 to 18 years.
- Calculated HJC locations using sphere-fitting to the acetabulum.
- Compared three HJC estimation techniques: regression equations, linear scaling (paediatric and adult bone), and a paediatric statistical shape model.
Main Results:
- Paediatric statistical shape model prediction yielded the lowest mean Euclidean distance error (2.95 mm ± 1.65 mm).
- Linear scaling of paediatric bone showed lower errors (3.90 mm ± 2.52 mm) compared to regression equations (6.23 mm ± 2.90 mm) and adult bone scaling (5.45 mm ± 3.26 mm).
- The newly developed regression equations resulted in a Euclidean distance error of 6.23 mm ± 2.90 mm.
Conclusions:
- Paediatric statistical shape model prediction is the most accurate method for HJC estimation in children.
- Linear scaling of a mean paediatric pelvis provides more accurate HJC estimates than regression equations.
- These findings offer improved non-invasive HJC estimation techniques for paediatric biomechanical analysis.
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