Background/Aim: Orthopaedic implant-related bone infection is commonly associated with increased bone remodelling, osteocyte-mediated bone matrix degradation and periosteal bone apposition. Accurate evaluation is critical for deciphering pathogenesis. However, preclinical research is often bottlenecked by qualitative or semi-quantitative scoring, which undermines reproducibility. This study established a robust, fully automated, multimodal framework to eliminate observer bias and provide high-resolution quantification of bone loss, periosteal reaction and morphological changes.

Methods: A trans-tibial infection model was used in mice using Staphylococcus aureus-coated implants.¹ Longitudinal in vivo micro-CT scans (Days 4, 7, and 12) were analysed using a novel, automated task-list-based segmentation protocol to independently quantify trabecular (Tb.), original cortical (Ct.) and periosteal reactive (Ps.) bone. Additionally, RGB trichrome staining and digital image analysis quantified morphological changes and osteocyte lacunar geometry,² while droplet digital PCR (ddPCR) provided absolute bacterial genome quantification.^{1, 3}

Results: Automated micro-CT analysis revealed significant trabecular destruction by day 12 (Tb.BV/TV, p < 0.05; Tb.N, p < 0.001; Tb.BMD, p < 0.05). In the cortical compartment, Ct.BV/TV in the infection group was significantly reduced vs. controls by day 12 (p < 0.01), with a progressive decline from day 4 (p < 0.01) and day 7 (p < 0.05). Similarly, cortical porosity (Ct.Po) was significantly elevated by day 12 vs. controls (p < 0.01) with an increase from day 4 (p < 0.01) and day 7 (p < 0.05). No significant differences were observed in Ct.BMD. However, a robust periosteal response was observed; Ps.BV/TV and Ps.BV/Ct.BV were significantly higher in the infection group by day 12 vs. controls (p < 0.05) and preceding timepoints (p < 0.05). RGB staining confirmed marked morphological changes and circular osteocyte lacunae shift (p < 0.0001). ddPCR established a precise correlation between absolute bacterial load and selected structural alterations.

Conclusions: This study presents a standardised evaluation framework that significantly enhances research efficiency and objectivity. By implementing automated segmentation, we eliminated inter-observer bias and the time-intensive nature of manual contouring. Integrating absolute bacterial quantification with precise histomorphometric analysis offers a transformative, fully quantitative perspective for musculoskeletal research and provides a rigorous methodological standard for assessing bone infection.