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Updated: Jun 19, 2026

In situ Compressive Loading and Correlative Noninvasive Imaging of the Bone-periodontal Ligament-tooth Fibrous Joint
Published on: March 7, 2014
Predicting osteosynthesis screw failure by peri-implant bone morphology in multiple loading conditions
Juan D Silva-Henao1, Dieter H Pahr1, Andreas G Reisinger1
1Karl Landsteiner University of Health Sciences, Department of Anatomy and Biomechanics, Division Biomechanics, Dr. Karl-Dorrek-straße 30, 3500, Krems, Austria; Institute of Lightweight Design and Structural Biomechanics, Technische Universität Wien, Vienna, Austria.
Abstract:
Osteosynthesis screws are critical in orthopaedic surgery for stabilizing and aligning bone fracture fragments. Despite their importance, screw failure remains a significant complication, often due to excessive movement at the implant-bone interface resulting from both physiological loading and external mechanical forces. This study aims to enhance understanding of screw failure mechanisms by investigating the relationship between peri-implant CT-based trabecular bone morphology and screw failure under axial-, shear-, and mixed loading conditions, including the effect of plate elevation. Using high-resolution micro-computed tomography (micro-CT) and mechanical testing, 100 porcine epiphyseal bone samples were extracted and analysed to measure key CT-based trabecular morphometric indices and correlate them with mechanical failure. The study tested screws under ten different loading configurations. Statistical analyses revealed that bone volume (BV) and bone volume over total volume (BV/TV) are strong predictors of screw failure force, explaining 70-90 % of the variance in failure forces across different loading scenarios. The findings suggest that BV and BV/TV can be used to determine optimal screw implantation sites based on local bone morphology, potentially improving surgical outcomes and reducing postoperative complications. This research contributes to a more comprehensive understanding of orthopaedic screw behaviour and offers a predictive model for clinical use.
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