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Prediction of the Proximal Humerus Morphology Based on a Statistical Shape Model with Two Parameters: Comparison to
Florianne E van Schaardenburgh1, H Chien Nguyen2,3, Joëll Magré2,3
1Orthopaedic Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Bioengineering (Basel, Switzerland)
|October 28, 2023
Summary
A new Statistical Shape Model (SSM) accurately predicts proximal humerus segments, offering an alternative to contralateral registration for complex fractures when healthy anatomy is unavailable.
Area of Science:
- Orthopedic surgery
- Medical imaging
- Biomechanical modeling
Background:
- Complex proximal humerus fractures can lead to complications post-surgery.
- Understanding 3D displacement is crucial for fracture morphology analysis.
- Contralateral humerus imaging is typically used for surgical reconstruction, but requires healthy contralateral anatomy.
Purpose of the Study:
- To develop a Statistical Shape Model (SSM) for predicting the proximal humerus segment.
- To compare the SSM's predictive effectiveness against the contralateral registration method.
Main Methods:
- An SSM was constructed using 137 healthy humeri datasets.
- Predictions for proximal humerus segments were generated using the SSM combined with parameters.
- The SSM predictions and contralateral registration results were compared against actual patient data.
Main Results:
- The developed SSM captured 95% of anatomical variation within eight principal modes.
- Both SSM prediction and contralateral registration methods demonstrated deviations generally below the 2 mm clinical threshold.
- The SSM proved effective in predicting humeral head geometry.
Conclusions:
- A Statistical Shape Model (SSM) combined with parameters is a viable method for predicting proximal humeral segments.
- This SSM approach is particularly useful when contralateral CT scans are unavailable or the contralateral humerus is compromised.
- The model's applicability is contingent on the fracture pattern allowing for necessary parameter measurements.
