Using artificial neural networks to predict impingement and dislocation in total hip arthroplasty

Area of Science:

• Biomedical Engineering
• Computational Mechanics
• Artificial Intelligence in Medicine

Background:

• Dislocation is a significant complication following total hip arthroplasty (THA).
• Prosthesis design (head size) and acetabular cup positioning (anteversion, abduction) critically influence impingement and dislocation risk.
• Patient movement, particularly external extension and internal flexion, are key factors in dislocation events.

Purpose of the Study:

• To develop a computational tool for predicting the range of motion (ROM) before impingement and dislocation in THA.
• To assist clinicians in selecting optimal prosthesis designs and acetabular cup positions to minimize dislocation probability.
• To validate the computational tool's accuracy against finite element simulations and clinical dislocation cases.

Main Methods:

• Development of a three-dimensional (3D) parametric finite element (FE) model of a total hip arthroplasty.
• Inclusion of femoral head size and acetabular abduction/anteversion angles as model parameters.
• Training an artificial neural network (ANN) using FE simulation data to predict ROM, validated against patient data.

Main Results:

• The artificial neural network (ANN) achieved high accuracy, with absolute errors below 5.5° compared to FE simulations for ROM prediction.
• The computational tool successfully predicted ROM for various THA configurations.
• Predictions for patients with prior dislocations correlated with their clinical outcomes, confirming the tool's predictive capability.

Conclusions:

• The integrated 3D FE model and ANN provide a valuable computational approach for predicting ROM in total hip arthroplasty.
• This tool can guide the selection of prosthesis head designs and acetabular cup placements to reduce impingement and dislocation.
• The findings support the use of computational modeling in optimizing THA surgery and improving patient safety.