Affiliations 

  • 1 School of Aerospace, Mechanical and Mechatronic Engineering, the University of Sydney, Sydney, 2006, Australia
  • 2 Faculty of Dentistry and Integrative Pharmacogenomics Institute, Universiti Teknologi MARA, Selangor, 40450, Malaysia
  • 3 College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QJ, UK
  • 4 Institute for Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, EH14 4AS, UK
Adv Healthc Mater, 2024 Sep;13(22):e2400091.
PMID: 38722148 DOI: 10.1002/adhm.202400091

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, this work develops 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 RRR 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 the understanding of the mechanobiological responses in the soft-hard tissue interfaces and the role in bone remodeling.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.