Accuracy Quantification of the Reverse Engineering and High-Order Finite Element Analysis of Equine MC3 Forelimb.
Authors: Mouloodi Saeed, Rahmanpanah Hadi, Burvill Colin, Davies Helen M S
Journal: Journal of equine veterinary science
Summary
Accurate digital models of equine bones are essential for understanding how shape influences biomechanical responses and adaptation, yet published finite element analyses (FEA) of equine skeletal structures typically report error margins of 3–5%, which may be inadequate for clinical decision-making. Mouloodi and colleagues reconstructed the three-dimensional geometry of 15 equine third metacarpal bones using reverse engineering from cross-sectional slices, then compared these models against computed tomography-based reference images to quantify reconstruction accuracy and identify sources of error throughout the bone's surface and cortices. The reconstructed models demonstrated remarkably low geometric error, with mean deviations of ±0.407 mm on convex surfaces and ±0.563 mm on concave surfaces, whilst dorsal cortical measurements showed even tighter accuracy (±0.216 mm convex, ±0.185 mm concave). Through displacement-based error estimation and finite element convergence analysis on 10 MC3 specimens, the authors developed a protocol that achieves FEA accuracy to within 0.12%—substantially lower than conventionally reported figures—without computational penalties. For practitioners involved in lameness diagnosis, rehabilitation planning, and orthopaedic surgery, these validated modelling techniques offer a pathway to biomechanical simulations of sufficient precision to inform evidence-based treatment strategies tailored to individual bone morphology.
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Practical Takeaways
- •CAD-based reverse engineering can accurately reconstruct equine MC3 bone geometry for biomechanical modelling with errors <0.6 mm, supporting virtual analysis of forelimb loading and remodelling.
- •High-accuracy finite element analysis (0.12% error) is achievable for equine bone studies, enabling better prediction of stress distribution and potential injury risk patterns.
- •This methodology could improve understanding of how bone shape changes in response to athletic demands, informing farriery and training practices.
Key Findings
- •Reverse engineering reconstruction of equine MC3 bones achieved minimum surface errors of +0.135 mm to -0.185 mm with mean errors of 0.407 mm ± 0.235 (convex) and -0.563 mm ± 0.369 (concave).
- •Dorsal cortex showed lowest reconstruction error at 0.216 mm ± 0.07 (outside) and -0.185 mm ± 0.13 (inside surface).
- •Proposed finite element analysis model achieved 0.12% error compared to literature values of 3-5%, requiring fewer iterations for convergence.