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  4. Reconstruction of dental roots for implant planning purposes
 
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2022
Journal Article
Titel

Reconstruction of dental roots for implant planning purposes

Titel Supplements
A feasibility study
Abstract
Purpose: Modern virtual implant planning is a time-consuming procedure, requiring a careful assessment of prosthetic and anatomical factors within a three-dimensional dataset. In order to facilitate the planning process and provide additional information, this study examines a statistical shape model (SSM) to compute the course of dental roots based on a surface scan. Material and methods: Plaster models of orthognathic patients were scanned and superimposed with three-dimensional data of a cone-beam computer tomography (CBCT). Based on the open-source software “R”, including the packages Morpho, mesheR, Rvcg and RvtkStatismo, an SSM was generated to estimate the tooth axes. The accuracy of the calculated tooth axes was determined using a leave-one-out cross-validation. The deviation of tooth axis prediction in terms of angle or horizontal shift is described with mean and standard deviation. The planning dataset of an implant surgery patient was additionally analyzed using the SSM. Results: 71 datasets were included in this study. The mean angle between the estimated tooth-axis and the actual tooth-axis was 7.5 ± 4.3° in the upper jaw and 6.7 ± 3.8° in the lower jaw. The horizontal deviation between the tooth axis and estimated axis was 1.3 ± 0.8 mm close to the cementoenamel junction, and 0.7 ± 0.5 mm in the apical third of the root. Results for models with one missing tooth did not differ significantly. In the clinical dataset, the SSM could give a reasonable aid for implant positioning. Conclusions: With the presented SSM, the approximate course of dental roots can be predicted based on a surface scan. There was no difference in predicting the tooth axis of existent or missing teeth. In clinical context, the estimation of tooth axes of missing teeth could serve as a reference for implant positioning. However, a higher number of training data must be achieved to obtain increasing accuracy.
Author(s)
Brandenburg, L.S.
Universitätsklinikum Freiburg
Berger, L.
Albert Ludwigs Universität Freiburg, Medizinische Fakultät
Schwarz, S.J.
Universitätsklinikum Freiburg
Meine, Hans
Fraunhofer-Institut für Digitale Medizin MEVIS
Weingart, J.V.
Universitätsklinikum Freiburg
Steybe, D.
Universitätsklinikum Freiburg
Spies, B.C.
Universitätsklinikum Freiburg
Burkhardt, F.
Universitätsklinikum Freiburg
Schlager S., S.
Universitätsklinikum Freiburg
Metzger, M.C.
Universitätsklinikum Freiburg
Zeitschrift
International journal of computer assisted radiology and surgery
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DOI
10.1007/s11548-022-02716-x
Language
English
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Fraunhofer-Institut für Digitale Medizin MEVIS
Tags
  • Anatomical reconstruc...

  • Implant planning

  • Statistical shape mod...

  • Virtual planning

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