Validation of a Patient Specific Statistical Shape Model for Predicting Pre-Morbid Glenoid Morphology

Description

In this presentation, Cole Fleet discusses a study validating a patient-specific statistical model developed for predicting premorbid glenoid morphology in the context of total shoulder arthroplasty (TSA). He begins by explaining the importance of properly positioning the glenoid implant to prevent complications such as implant failure due to poor component positioning. Fleet introduces Statistical Shape Modeling as an emerging technique for predicting premorbid glenoid bone structures, based on a statistical model generated from 85 healthy CT scans.



The methodology includes creating virtually eroded scapular models categorized by various erosion types using the Walch and Favard classification systems, ultimately resulting in 202 different models. Each model underwent verification by an experienced surgeon before analysis with surgical planning software. The core results demonstrated the model's effectiveness in accurately restoring glenoid inclination, version, and center position, with only minor discrepancies noted. Fleet emphasizes that this study is the first of its kind to validate the use of a scapular Statistical Shape Model specifically for predicting the premorbid glenoid structure, compared directly against established scapular values.



Ultimately, Fleet concludes that this model can be a significant asset in preoperative planning for shoulder surgeries, especially for patients experiencing glenoid bone loss.

DOI: 10.1302/3114-220966

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