aceMachine 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 annotat…
A trained segmentation model gives pixel-level, full-surface measurement of corrosion and mechanical damage on femoral stem tapers, replacing single-grade visual scoring and cutting manual effort on large SEM datasets.
The framework was applied only as a proof of concept to two representative taper cases, and mechanical damage segmentation was weaker (IoU 0.496) than corrosion (0.678).
Evidence
- Peer-reviewedJournal of the Mechanical Behavior of Biomedical Materials2026-10-12
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Truvace Impact Record TRV-2026-2053, v1: “Machine learning-based detection and area quantification of mechanical and corrosion damage in femoral stem tapers.” Truvace, 2026-10-20. /record/TRV-2026-2053 (accessed at citation time). sha256 aa045d19d88f689f…
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