Surface Characterisation of Retrieved Orthopaedic Knee Liners

Total knee arthroplasty (TKA) is one of the most frequently performed surgical procedures for patients with advanced knee joint disease, which is intended to relieve pain and restore normal joint function. A critical component of the TKA system is the ultra-high-molecular-weight polyethylene knee liner, which acts as the bearing surface between the metallic components. Despite continuous improvements in material processing and implant design, these liners remain vulnerable to several damage mechanisms such as wear, fatigue, delamination, oxidative degradation, pitting, embedded debris, overload, creep, edge damage, backside wear, and fracture. This study introduces a new quadrant-based characterization system to evaluate retrieved knee liners through non-destructive methods. The liners, collected from revision surgeries, were divided into nine anatomical zones labelled Q1 to Q9 to systematically identify and map surface damage. Damage density was determined manually as well as by using computational image analysis through MATLAB R2024a and Python 3.13. The computational methods demonstrated greater accuracy and reproducibility, showing a strong correlation with manual evaluation, with p equalling 0.41 for Python and p equalling 1.00 for MATLAB. The proposed quadrant-based system, together with computational validation, offers a more reliable framework in studying wear and damage patterns in retrieved implants. This approach contributes to an enhanced understanding of how different damage modes interact and offers useful guidance for enhancing implant design, material durability, and clinical outcome improvement in total knee arthroplasty.