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Published on: October 18, 2021
Comparative Finite Element Analysis of Short Implants and Lateralization of the Inferior Alveolar Nerve With
Sérgio J Jayme1, Paulo R Ramalho, Leonardo De Franco
1*Department of Implantology, Paulista Association of Dental Surgeons, Vila Mariana †Department of Periodontology and Oral Implantology, Dental Research Division, Head of Oral Implantology Clinic, University of Guarulhos, Guarulhos, São Paulo ‡Private Consultant, Curitiba, Brazil.
Abstract:
The lateralization of the inferior alveolar nerve (LIAN) and short implants are efficient options for rehabilitation of the posterior atrophic mandible. However, the loss of bone leads to prosthesis with greater height and lever effect that in turn can have different impact on treatments. Through the finite element method, the present study tests the hypothesis that conventional implants placed under LIAN and short implants have similar risk of bone loss regarding variable height of the crown and that crown-to-implant ratio is not a reliable resource to evaluate risk in these treatments. Computed tomography scans of mandibles were processed and implants and prosthetic components were reverse engineered for reconstruction of three-dimensional models to simulate 3 elements fixed partial dentures supported by 2 osseointegrated implants. The models of implants were based on MK III implants (Nobel Biocare, Zurich, Switzerland) with 4 mm in diameter by 7 mm in length representing short implants, and 15 mm in length representing implants used in LIAN. The implant/crown ratio for short implants was 1:1.5, 1:2, and 1:2.5 and LIAN models were modeled with exactly the same prosthesis, resulting in implant/crown ratios of 1:0.67, 1:0.89, and 1:1.12. The results partially rejected the hypothesis that LIAN and short implants have similar risk of bone loss, showing that although LIAN results were better in the models evaluated, the variations in height had proportionally similar impact on both treatments and accepted the hypothesis that crown-to-implant ratio was not a reliable resource to evaluate risk.
