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A scapular statistical shape model can reliably predict premorbid glenoid morphology in conditions of severe glenoid
Cole T Fleet1, Théo Giraudon2, Gilles Walch3
1Department of Mechanical and Materials Engineering, Western University, London, Canada.
Background:
Knowledge of premorbid glenoid parameters at the time of shoulder arthroplasty, such as inclination, version, joint line position, height, and width, can assist with implant selection, implant positioning, metal augment sizing, and/or bone graft dimensions. The objective of this study was to validate a scapular statistical shape model (SSM) in predicting patient-specific glenoid morphology in scapulae with clinically relevant glenoid erosion patterns.
Methods:
Computed tomography scans of 30 healthy scapulae were obtained and used as the control group. Each scapula was then virtually eroded to create 7 erosion patterns (Walch A1, A2, B2, B3, D, Favard E2, and E3). This resulted in 210 uniquely eroded glenoid models, forming the eroded glenoid group. A scapular SSM, created from a different database of 85 healthy scapulae, was then applied to each eroded scapula to predict the premorbid glenoid morphology. The premorbid glenoid inclination, version, height, width, radius of best-fit sphere, and glenoid joint line position were automatically calculated for each of the 210 eroded glenoids. The mean values for all outcome variables were compared across all erosion types between the healthy, eroded, and SSM-predicted groups using a 2-way repeated measures analysis of variance.
Results:
The SSM was able to predict the mean premorbid glenoid parameters of the eroded glenoids with a mean absolute difference of 3° ± 2° for inclination, 3° ± 2° for version, 2 ± 1 mm for glenoid height, 2 ± 1 mm for glenoid width, 5 ± 4 mm for radius of best-fit sphere, and 1 ± 1 mm for glenoid joint line. The mean SSM-predicted values for inclination, version, height, width, and radius were not significantly different than the control group (P > .05).
Discussion:
An SSM has been developed that can reliably predict premorbid glenoid morphology and glenoid indices in patients with common glenoid erosion patterns. This technology can serve as a useful template to visually represent the premorbid healthy glenoid in patients with severe glenoid bony erosions. Knowledge of the premorbid glenoid preoperatively can assist with implant selection, positioning, and sizing.
