An intelligent system for image-based rating of corrosion severity at stem taper of retrieved hip replacement implants

Visual scoring of damage at taper junctions is the sole method to quantify corrosion in large-scale retrieval studies of failed hip replacement implants. This study introduces an intelligent image analysis-based method that objectively rates corrosion at stem taper of retrieved hip implants according to the well-known Goldberg scoring method. A Support Vector Machine classifier was used that takes in vectors of global and local textural features and assigns scores to the corresponding images. Bayesian optimisation fine-tunes the hyperparameters of the classifier to minimise the cross-validation error.