A Soft-Tissue Driven Bone Remodeling Algorithm for Mandibular Residual Ridge Resorption Based on Patient CT Image Data

Jingxiao Zhong, Wenwei Huang, Rohana Ahmad, Junning Chen, Chi Wu, Jingrui Hu, Keke Zheng, Michael V. Swain, Qing Li

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Abstract

The role of the biomechanical stimulation generated from soft tissue has not been well quantified or separated from the self-regulated hard tissue remodeling governed by Wolff's Law. Prosthodontic overdentures, commonly used to restore masticatory functions, can cause localized ischemia and inflammation as they often compress patients' oral mucosa and impede local circulation. This biomechanical stimulus in mucosa is found to accelerate the self-regulated residual ridge resorption (RRR), posing ongoing clinical challenges. Based on the dedicated long-term clinical datasets, we developed an in-silico framework with a combination of techniques, including advanced image post-processing, patient-specific finite element models and unsupervised machine learning Self-Organizing map algorithm, to identify the soft tissue induced residual ridge resorption and quantitatively elucidate the governing relationship between the RRR and hydrostatic pressure in mucosa. The proposed governing equation has not only enabled a predictive simulation for RRR as showcased in this study, providing a biomechanical basis for optimizing prosthodontic treatments, but also extended our understanding of the mechanobiological responses in the soft-hard tissue interfaces and the role in bone remodeling. This article is protected by copyright. All rights reserved.

Original languageEnglish
Article numbere2400091
JournalAdvanced Healthcare Materials
Early online date9 May 2024
DOIs
Publication statusE-pub ahead of print - 9 May 2024

Keywords

  • Machine learning
  • Mechanobiology
  • Predictive simulation
  • Soft‐tissue induced bone remodeling
  • Spatial image quantification

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