Predictive Models for Pre-operative Diagnosis of Rotator Cuff Tear: A Comparison Study of Two Methods between Logistic Regression and Artificial Neural Network

摘要

Rotator cuff tears are the most common disorder of the shoulders agnetic resonance Image (MRI) is the diagnostic gold standard of rotator cuff tears. However, there are some dilemmas in the rotator cuff tears treatment. Clinically, surgical results of rotator cuff tears are sometimes different from MRI results of rotator cuff tears. The main purpose of this study is to build up predicative models for pre-operative diagnosis of rotator cuff tears There are two models of this study are proposed: logistic regression model and artificial neural network model. Patients are divided into two sets: Setl is patients with full thickness rotators cuff tears. Set 2 is patients with partial thickness rotators cuff tears. The charts of 158 patients are completely reviewed and the collected data were analyzed. The results showed that the predictive accuracy of artificial neural networks model is higher than the predictive accuracy of logistic model. The application of this study can assist doctors to increase the accuracy rate of pre-operative diagnosis and to decrease the legal problems.