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A novel computational method to determine subject-specific bite force and occlusal loading during mastication.

Oliver RöhrleHarnoor SainiPeter V S LeeDavid C Ackland
Published in: Computer methods in biomechanics and biomedical engineering (2018)
The evaluation of three-dimensional occlusal loading during biting and chewing may assist in development of new dental materials, in designing effective and long-lasting restorations such as crowns and bridges, and for evaluating functional performance of prosthodontic components such as dental and/or maxillofacial implants. At present, little is known about the dynamic force and pressure distributions at the occlusal surface during mastication, as these quantities cannot be measured directly. The aim of this study was to evaluate subject-specific occlusal loading forces during mastication using accurate jaw motion measurements. Motion data was obtained from experiments in which an individual performed maximal effort dynamic chewing cycles on a rubber sample with known mechanical properties. A finite element model simulation of one recorded chewing cycle was then performed to evaluate the deformation of the rubber. This was achieved by imposing the measured jaw motions on a three-dimensional geometric surface model of the subject's dental impressions. Based on the rubber's deformation and its material behaviour, the simulation was used to compute the resulting stresses within the rubber as well as the contact pressures and forces on the occlusal surfaces. An advantage of this novel modelling approach is that dynamic occlusal pressure maps and biting forces may be predicted with high accuracy and resolution at each time step throughout the chewing cycle. Depending on the motion capture technique and the speed of simulation, the methodology may be automated in such a way that it can be performed chair-side. The present study demonstrates a novel modelling methodology for evaluating dynamic occlusal loading during biting or chewing.
Keyphrases
  • finite element
  • single molecule
  • oral health
  • high speed
  • machine learning
  • deep learning
  • artificial intelligence
  • pseudomonas aeruginosa
  • biofilm formation
  • candida albicans