Machine learning-based detection and area quantification of mechanical and corrosion damage in femoral stem tapers.

Corrosion and mechanical damage at modular femoral taper junctions remain important degradation mechanisms in total hip arthroplasty. Yet, their assessment commonly relies on ordinal visual scoring methods that reduce spatially heterogeneous damage to a single severity grade. Here, we present a SegFormer-based semantic segmentation framework for pixel-level identification and quantitative assessment of tribocorrosion damage on Ti6Al4V tapered shafts following controlled hip simulator testing. We manually annotated a dataset of 576 high-resolution scanning electron microscopy (SEM) images into background, corrosion, and mechanical damage classes, using representative SEM and energy-dispersive X-ray spectroscopy analyses and literature for damage feature identification. The model was trained using a hybrid weighted cross-entropy and Dice loss, and a structured three-stage hyperparameter optimization strategy. Five independently seeded models were subsequently combined for ensemble inference to improve full-field damage quantification and assess seed-dependent prediction variability. On the independent test set, the final model achieved IoU values of 0.678 for corrosion and 0.496 for mechanical damage, with an overall pixel accuracy of 0.947. As a proof-of-concept application, the developed framework was applied to two representative taper cases, consisting of Ti6Al4V shafts coupled with either Ti6Al4V or CoCrMo femoral heads, to demonstrate full-field damage quantification. Ensemble analysis showed that the representative Ti6Al4V/CoCrMo case exhibited a greater corrosion area and more spatially concentrated corrosion than the Ti6Al4V/Ti6Al4V case. Region-size analysis further revealed that larger localized regions contributed substantially to the total corrosion area, including a single 0.50 to 1.00 mm