Refining experimental dental implant testing in the Göttingen Minipig using 3D computed tomography-A morphometric study of the mandibular canal.
Giuliano Mario CorteJohanna PlendlHana HünigenKenneth C RichardsonOle GemeinhardtStefan M NiehuesPublished in: PloS one (2017)
This study reports morphometric and age-related data of the mandibular canal and the alveolar ridge of the Göttingen Minipig to avoid complications during in vivo testing of endosseus dental implants and to compare these data with the human anatomy. Using 3D computed tomography, six parameters of the mandibular canal as well as the alveolar bone height and the alveolar ridge width were measured in Göttingen Minipigs aged 12, 17 and 21 months. Our null hypothesis assumes that the age and the body mass have an influence on the parameters measured. The study found that the volume, length and depth of the mandibular canal all increase with age. The width of the canal does not change significantly with age. The body mass does not have an influence on any of the measured parameters. The increase in canal volume appears to be due to loss of deep spongy bone in the posterior premolar and molar regions. This reduces the available space for dental implantations, negatively affecting implant stability and potentially the integrity of the inferior alveolar neurovascular bundle. Dynamic anatomical changes occur until 21 months. On ethical grounds, using minipigs younger than 21 months in experimental implant dentistry is inadvisable. Paradoxically the measurements of the 12 months old pigs indicate a closer alignment of their mandibular anatomy to that of humans suggesting that they may be better models for implant studies. Given the variability in mandibular canal dimensions in similar age cohorts, the use of imaging techniques is essential for the selection of individual minipigs for dental prosthetic interventions and thus higher success rates.
Keyphrases
- computed tomography
- soft tissue
- magnetic resonance imaging
- body mass index
- bone mineral density
- cone beam computed tomography
- machine learning
- electronic health record
- risk factors
- magnetic resonance
- artificial intelligence
- mass spectrometry
- body composition
- decision making
- image quality
- pet ct
- pluripotent stem cells