Bibliographic Details
| Title: |
Three-Dimensional Simulation of Mandibular Distraction Osteogenesis: Mechanobiological Analysis. |
| Authors: |
Reina-Romo, E.1 erreina@us.es, Gómez-Benito, M.2 gomezmj@unizar.es, Sampietro-Fuentes, A.3, Domínguez, J.1 jaime@us.es, García-Aznar, J.2 jmgaraz@unizar.es |
| Source: |
Annals of Biomedical Engineering. Jan2011, Vol. 39 Issue 1, p35-43. 9p. |
| Subjects: |
Temporomandibular joint abnormalities, Bone mechanics, Goldenhar syndrome, Osteoradiography, Bone growth, Health outcome assessment, Finite element method |
| Abstract: |
Distraction osteogenesis is a surgical process for reconstruction of skeletal deformities, which has been widely investigated from the clinical perspective. However, little has been analyzed about the capability of numerical models to predict the clinical outcome generated by distraction. Therefore, this article presents a finite element analysis of the mechanobiological behavior of a pediatric patient's mandible with hemifacial microsomia during the distraction process. It focuses on the three-dimensional simulation of a long bone defect in the ramus of the mandible and introduces additional aspects to be considered in the computational simulation as compared to the bidimensional simulation. The evolution of the different tissues within the gap is evaluated and in order to check the effectiveness of the model, the predicted numerical outcome will be compared from a qualitative point of view with radiographies provided by the surgeons. It is shown that the morphology of the mandible changed in a similar manner than that observed clinically. These results reveal that three-dimensional models are useful tools in the predictive assessment of mandibular distraction osteogenesis. [ABSTRACT FROM AUTHOR] |
|
Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |
