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Background <p>The anterior component of force (ACF) is key to proximal contact loss (PCL) after implant restoration, yet its quantitative impact remains unclear. This study employed digital model analysis to indirectly assess the effects of ACF and examine its association with PCL.</p> Methods <p>Following final restoration, 3D surface and buccal occlusal data were obtained from ninety first molar implant sites and mesial adjacent teeth. Occlusal force-induced changes in proximal contact gap (Δd<sub>P</sub>) and centroid position (Δd<sub>C</sub>) were measured using Geomagic Wrap 2021 for indirect ACF estimation. Proximal contact gaps were measured under non‑occluding conditions at baseline and 6 months to calculate the change over time (ΔD). Univariate and multivariate analyses identified PCL-related biomechanical influencing factors, and a restricted cubic spline (RCS) model was developed and validated using Cohen’s Kappa.</p> Results <p>The 6-month PCL incidence was 17.78%, and Δd<sub>P</sub> emerged as a significant biomechanical influencing factor of ΔD (<i>P</i> &lt; 0.001). RCS analysis revealed that the adjacent state remains stable when Δd<sub>P</sub> is within the range of − 13&#xa0;μm to 9&#xa0;μm; beyond this range, imbalance occurs. The model demonstrated good fitting capability (<i>κ</i> = 0.59, <i>P</i> = 0.003).</p> Conclusion <p>This study achieved an indirect assessment and quantification of the effect of ACF through digital model analysis technology, and clarified its correlation with the PCL of the implant, providing new evidence and methodological references for related biomechanical research.</p>

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Digital quantitative analysis of biomechanical factors of proximal contact loss of first molar implant crown

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • Derong Yin,
  • Lingrui Gao,
  • Hongwei Gao,
  • Lijun Xue,
  • Zhongda Wang,
  • Meie Jia,
  • Feng Wu

摘要

Background

The anterior component of force (ACF) is key to proximal contact loss (PCL) after implant restoration, yet its quantitative impact remains unclear. This study employed digital model analysis to indirectly assess the effects of ACF and examine its association with PCL.

Methods

Following final restoration, 3D surface and buccal occlusal data were obtained from ninety first molar implant sites and mesial adjacent teeth. Occlusal force-induced changes in proximal contact gap (ΔdP) and centroid position (ΔdC) were measured using Geomagic Wrap 2021 for indirect ACF estimation. Proximal contact gaps were measured under non‑occluding conditions at baseline and 6 months to calculate the change over time (ΔD). Univariate and multivariate analyses identified PCL-related biomechanical influencing factors, and a restricted cubic spline (RCS) model was developed and validated using Cohen’s Kappa.

Results

The 6-month PCL incidence was 17.78%, and ΔdP emerged as a significant biomechanical influencing factor of ΔD (P < 0.001). RCS analysis revealed that the adjacent state remains stable when ΔdP is within the range of − 13 μm to 9 μm; beyond this range, imbalance occurs. The model demonstrated good fitting capability (κ = 0.59, P = 0.003).

Conclusion

This study achieved an indirect assessment and quantification of the effect of ACF through digital model analysis technology, and clarified its correlation with the PCL of the implant, providing new evidence and methodological references for related biomechanical research.