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Orthopaedics & Traumatology: Surgery & Research
Sous presse. Epreuves corrigées par l'auteur. Disponible en ligne depuis le mercredi 13 février 2019
Doi : 10.1016/j.otsr.2018.10.024
Received : 12 February 2018 ;  accepted : 25 October 2018
Thoughts on a new surgical assistance method for implanting the glenoid component during total shoulder arthroplasty. Part 1: Statistical modeling of the native premorbid glenoid
 

Julien Berhouet b, c, , Luc Favard a, c, David Boas b, Théo Voisin b, Mohamed Slimane b
a Service d’orthopédie traumatologie, faculté de médecine de Tours, université François Rabelais de Tours, CHRU Trousseau, 1C, avenue de la République, 37170 Chambray-les-Tours, France 
b Équipe reconnaissance de forme et analyse de l’image, université François Rabelais de Tours, école d’ingénieurs polytechnique universitaire de Tours, laboratoire d’informatique EA6300, 64, avenue Portalis, 37200 Tours, France 
c Western France Orthopedics Society (SOO)/HUGORTHO, 18, rue de Bellinière, 49800 Trélazé, France 

Corresponding author. Service d’orthopédie traumatologie, faculté de médecine de Tours, université François Rabelais de Tours, CHRU Trousseau, 1C, avenue de la République, 37170 Chambray-les-Tours, France.France
Abstract
Introduction

The aim of this study was to identify points on the scapula that can be used to predict the anatomy of the native premorbid glenoid.

Material and methods

Forty-three normal scapulas reconstructed in 3D and positioned in a common coordinate system were used. Twenty points distributed over the blade of the scapula (portion considered normal and used as a reference) and the glenoid (portion considered pathological and needing to be reconstructed) were captured manually. Thirteen distances (X) between two points not on the glenoid and 31 distances (Y) between two points of which at least one was on the glenoid were then calculated automatically. A multiple linear regression model was applied to calculate the Y distances from the X distances. The best four equations were retained based on their coefficient of determination (R2) to explain a point on the glenoid being reconstructed (p <0.05). In the first scenario, the glenoid was modeled assuming it was completely destroyed. In the second scenario, only the inferior portion of the glenoid was worn.

Results

For a completely destroyed glenoid, the mean error for a chosen distance for a given point on the glenoid was 2.4 mm (4.e-3mm; 12.5mm). For a partially damaged glenoid, the mean error was 1.7mm (4.e-3mm; 6.5mm) for the same distance evaluated for a given point on the glenoid.

Discussion/Conclusion

The proposed statistical model was used to predict the premorbid anatomy of the glenoid with an acceptable level of accuracy. A surgeon could use this information during the preoperative planning stage and during the actual surgery by using a new surgical assistance method.

The full text of this article is available in PDF format.

Keywords : Premorbid glenoid, Multiple linear regression, Statistical prediction, Surgical assistance, Shoulder arthroplasty



 Article issued from the Orthopaedics and Traumatology Society of Western France (SOO) – 2017 Tours meeting.



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