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Predisposing Factors for Orthodontic Mini-Implant Failure Defined by Bone Strains in Patient-Specific Finite Element Models

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Abstract

Factors responsible for the success or failure of orthodontic mini-implants (OMIs) in clinical settings are unclear. Failure of OMIs was found to be associated with increased maximum principal strain (MaxPN) when assessed using the subject-specific finite element (FE) modeling technique. The purpose of the present study was to identify factors that increase MaxPN and thereby predispose the OMI to failure. Using the FE method, MaxPN was calculated around 28 OMIs placed in orthodontic patients, 6 of which failed during the first 5 months. Sixteen potential risk factors related to patients or to OMI position were measured on computerized tomographic images or calculated in FE models. The impact of these factors on MaxPN was verified using regression analysis. Three factors were found to have significant nonlinear relationships with MaxPN: cortical bone quality, vertical angulation of the OMI, and proximity of the OMI to the tooth in the direction of force. In conclusion, failure of an OMI is a multifactorial problem, and position and angulation of the implant are among the affecting factors. Slight apical inclination and positioning at least 1 mm off the root in the direction of force may significantly decrease failure probability.

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Acknowledgements

This study was supported by Faculty of Dental Science Kyushu University. The authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article.

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Correspondence to Ichiro Takahashi.

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Associate Editor Eiji Tanaka oversaw the review of this article.

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Figure S1

Horizontal sections in all cases showing distribution of maximum principal strain. Cases denoted with F are the failed cases, and those denoted with S are successful ones. Supplementary material 1 (TIFF 2394 kb)

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Albogha, M.H., Kitahara, T., Todo, M. et al. Predisposing Factors for Orthodontic Mini-Implant Failure Defined by Bone Strains in Patient-Specific Finite Element Models. Ann Biomed Eng 44, 2948–2956 (2016). https://doi.org/10.1007/s10439-016-1584-8

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