Évaluation morpho-fonctionnelle de l'épaule

Abstract

Shoulder issues are often associated with rotator cuff tears. The litterature studies the risk factors associated with the development of the pathology but only a few studies are interested in the factors associated with the shoulder function. The objective of the study is to find a combinations of both radiological and morphological parameters which could explain the functional outcomes of a shoulder with rotator cuff tears. The first part of this master’s thesis will focus on the identification of the parameters which are most often associated with rotator cuff tears. Then, the different methods used to acquire morphological parameters will be described. A bi-planar low dose X-Ray method for personalized 3D modeling of the shoulder and automated computation of morphological parameters will then be described. The accuracy and reliability of these parameters are then assessed. Then, we introduce and caracterise the data base of 80 subjects : 52 subjects with at least one massive supra-spinatus tear and 28 subjects without symptoms. Finally, with simple and multiple linear regression we assess the associations between radiological and morphological parameters and the shoulder function of the subjects. Simple regression analyses do not show any strong correlation between the shoulder function and one parameter. However, the multiple regression analysis allow us to build a regression model with 5 parameters : 1 radiological parameter and 4 morphological parameters. This regression model predicts 43,3% of the variability of the shoulder function. A regression model with only the radiological parameters only predicts 16,7% of the shoulder function. These results show that it is necessary to combine both radiological and morphological parameters to predict the function of a shoulder with a rotator cuff tear. This can lead to further explore predictive parameters or regression models.

Date	20 Aug 2018
Original language	French
Awarding Institution	École de technologie supérieure
Supervisor	Nicola Hagemeister (Supervisor) & Neila Mezghani (Co-supervisor)