There are two surgical approaches to performing total hip arthroplasty (THA): a cemented or uncemented type of prosthesis. The choice is usually based on the experience of the orthopaedic surgeon and on parameters such as the age and gender of the patient. Using machine learning (ML) techniques on quantitative biomechanical and bone quality data extracted from computed tomography, electromyography and gait analysis, the aim of this paper was, firstly, to help clinicians use patient-specific biomarkers from diagnostic exams in the prosthetic decision-making process. The second aim was to evaluate patient long-term outcomes by predicting the bone mineral density (BMD) of the proximal and distal parts of the femur using advanced image processing analysis techniques and ML. The ML analyses were performed on diagnostic patient data extracted from a national database of 51 THA patients using the Knime analytics platform. The classification analysis achieved 93% accuracy in choosing the type of prosthesis; the regression analysis on the BMD data showed a coefficient of determination of about 0.6. The start and stop of the electromyographic signals were identified as the best predictors. This study shows a patient-specific approach could be helpful in the decision-making process and provide clinicians with information regarding the follow up of patients.

The purpose of this study is to use Health Technology Assessment (HTA) through the Six Sigma (SS) and DMAIC (Define, Measure, Analyse, Improve, Control) problem-solving strategies for comparing cemented and uncemented prostheses in terms of the costs incurred for Total hip arthroplasty (THA) and the length of hospital stay (LOS). Multinomial logistic regression analysis for modelling the data was also performed. Quantitative parameters extracted from gait analysis, electromyography and computed tomography images were used to compare the approaches, but the analysis did not show statistical significance. The variables regarding costs were studied with the Mann-Whitney and Kruskal-Wallis tests. No statistically significant difference between cemented and uncemented prosthesis for the total cost of LOS was found, but the cost of the surgeon had an influence on the overall expenses, affecting the cemented prosthetic approach. The material costs of surgery for the uncemented prosthesis and the cost of theatre of surgery for the cemented prosthesis were the most influential. Multinomial logistic regression identified the Vastus Lateralis variable as statistically significant. The overall accuracy of the model is 93.0%. The use of SS and DMAIC cycle as tools of HTA proved that the cemented and uncemented approaches for THA have similar costs and LOSy.