Total Hip Arthroplasty (THA) is the gold standard for hip replacement surgery. It can be performed with two different kinds of prostheses: cemented and uncemented. The surgeons have always decided on the type of prosthesis based on the age, sex of the patient and bone stock on x rays. In this paper 42 subjects underwent THA and performed both gait analysis and bone mineral density (BMD) evaluation through CT scans; spatial and temporal gait parameters were used to predict BMD of the distal and proximal parts of the femur before and one year after surgery using machine learning regression analysis. A simple linear regression (LR) and k-nearest neighbor (KNN) were implemented coding with Python Scikit-Learn libraries and some evaluation metrics were computed: the coefficient of determination (R2), mean absolute error, mean squared error and root mean squared error. Both the algorithms had a R2 greater than 75% in predicting both proximal and distal; particularly, LR obtained the highest score of 88.4% in predicting the BMD before the THA and of 81.3% after the THA. All the R2 of KNN ranged from 75% and 77%. All the calculated errors were always below 0.001. In conclusion, this research shows the feasibility of gait parameters for assessing the follow-up after 52 weeks of patients undergoing THA by predicting the BMD. Moreover, the results give insights about the relationship between the patterns of gait and BMD.

A Regression Approach to Assess Bone Mineral Density of Patients undergoing Total Hip Arthroplasty through Gait Analysis

Esposito L.;
2020

Abstract

Total Hip Arthroplasty (THA) is the gold standard for hip replacement surgery. It can be performed with two different kinds of prostheses: cemented and uncemented. The surgeons have always decided on the type of prosthesis based on the age, sex of the patient and bone stock on x rays. In this paper 42 subjects underwent THA and performed both gait analysis and bone mineral density (BMD) evaluation through CT scans; spatial and temporal gait parameters were used to predict BMD of the distal and proximal parts of the femur before and one year after surgery using machine learning regression analysis. A simple linear regression (LR) and k-nearest neighbor (KNN) were implemented coding with Python Scikit-Learn libraries and some evaluation metrics were computed: the coefficient of determination (R2), mean absolute error, mean squared error and root mean squared error. Both the algorithms had a R2 greater than 75% in predicting both proximal and distal; particularly, LR obtained the highest score of 88.4% in predicting the BMD before the THA and of 81.3% after the THA. All the R2 of KNN ranged from 75% and 77%. All the calculated errors were always below 0.001. In conclusion, this research shows the feasibility of gait parameters for assessing the follow-up after 52 weeks of patients undergoing THA by predicting the BMD. Moreover, the results give insights about the relationship between the patterns of gait and BMD.
2020
978-1-7281-5386-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/434027
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