Prediction Of Normal Glenoid Morphology With Statistical Shape Modeling

Berger, Steve; Terrier, Alexandre; Becce, Fabio; Farron, Alain; Büchler, Philippe (2016). Prediction Of Normal Glenoid Morphology With Statistical Shape Modeling (Unpublished). In: Société Française de Chirurgie Orthopédique et Traumatologique. Paris.

Introduction
Patients suffering from glenohumeral osteoarthritis may present an important wear of the glenoid articular surface leading to a new glenoid shape (Neoglenoid). The amount of wear to correct can thus be difficult to evaluate when a total shoulder arthroplasty is planned. Since the positioning of the glenoid component is known to be critical for the long term success of the surgery, it would be helpful to have an estimate of the glenoid shape before it was deformed by osteoarthritis (Paleoglenoid).
The objective of this project was to evaluate the potential of statistical shape modeling to predict the shape of the articular glenoid surface from the remaining parts of the scapula, which have not been affected by the glenoid degenerative wear.

Materials and Methods
To build a statistical shape model (SSM) of the scapula, CT scans of 64 scapulae without any sign of pathology were segmented to produce smooth three-dimensional surfaces of triangular meshes. The 64 meshes were then registered and combined into a SSM using the Statismo library. To evaluate the accuracy of the SSM prediction, we repeated the following six steps for each of the 64 scapulae. Step 1: one of the 64 scapulae is selected. Step 2: the SSM is built without this scapula (leave-one-out). Step 3: the glenoid region of the selected scapula is removed. Step 4: the SSM is fitted based on the remaining part of the scapula. Step 5: the SSM predicts the removed glenoid region. Step 6: the local accuracy of the SSM is evaluated by the distance between the predicted and existing glenoid surfaces. The orientation of the original and reconstructed glenoid were also assessed.

Results
When the SSM was based on the lateral scapula, the average accuracy reached 0.6 ± 0.1 mm. The accuracy of the glenoid orientation prediction was 2.4 ± 1.6 degree.

Discussion & Conclusions
The proposed method allowed to predict the shape of the glenoid surface, which was here artificially removed. This prediction could be obtained from the lateral part of the scapula, glenoid excluded, which is not affected by wear caused by osteoarthritis. The accuracy of the reconstruction was below 1 mm, and thus acceptable for the preoperative planning of total shoulder arthroplasty. This method should help to improve the positioning of glenoid implants for severe glenoid wear, and eventually reduce the failure risk after total shoulder arthroplasty.

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