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Automatic virtual reconstruction of maxillofacial bone defects assisted by ICP (iterative closest point) algorithm and normal people database

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Clinical Oral Investigations Aims and scope Submit manuscript

A Correction to this article was published on 27 November 2021

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Abstract

Objectives

The aim of this study was to propose and validate an automatic approach based on iterative closest point algorithm for virtual complement and reconstruction for maxillofacial bone defects.

Materials and methods

A 3D craniomaxillofacial database of normal Chinese people including 500 skull models was established. Modified iterative closest point (ICP) algorithm was developed to complete bone defects automatically. The performances were evaluated by two approaches: (1) model experiment, virtual bony defects were created on 30 intact normal skull models not included in the database. For each defect model, the algorithm was applied to select the reference skull model from the database. 3-Dimensional and 2-dimensional comparison were conducted to evaluate the error between reference skull model with original intact model. Root mean square error (RMSE) and processing time were calculated. (2) Clinical application, the algorithm was utilized to assist reconstruction of 5 patients with maxillofacial bone defects. The symmetry of post-operative skull model was evaluated by comparing with its mirrored model.

Results

The algorithm was tested on an CPU with 1.80 GHz and average processing time was 493.5 s. (1) Model experiment, the average root-mean-square deviation of defect area was less than 2 mm. (2) Clinical application, the RMSE of post-operative skull and its mirrored model was 1.72 mm.

Conclusion

It is feasible using iterative closest point algorithm based on normal people database to automatically predict the reference data of missing maxillofacial bone.

Clinical relevance

An automated approach based on ICP algorithm and normal people database for maxillofacial bone defect reconstruction has been proposed and validated.

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Acknowledgements

The authors acknowledge Dr. Kaiyuan Fu, Dr. Chengyi Wang, and Dr. Yanhang Tong (Peking University School and Hospital of Stomatology) for develo** the normal people database.

Funding

This study was supported by National Key Research and Development Program of China (grant No. 2019YFF0302401&2017YFB1104103), National Natural Science Foundation of China (grant No. (82027807&81771940) and Bei**g Municipal Natural Science Foundation (7212202).

Author information

Author notes

  1. Bimeng Jie and Boxuan Han contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Road, Bei**g, 100081, China

    Bimeng Jie, Baocheng Yao, Yi Zhang & Yang He

  2. National Engineering Laboratory for Digital and Material Technology of Stomatology, Bei**g Key Laboratory of Digital Stomatology, National Clinical Research Center for Oral Diseases, Bei**g, China

    Bimeng Jie, Baocheng Yao, Yi Zhang & Yang He

  3. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Bei**g, 100084, China

    Boxuan Han & Hongen Liao

Authors
  1. Bimeng Jie
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  3. Baocheng Yao
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  4. Yi Zhang
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  5. Hongen Liao
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Corresponding authors

Correspondence to Hongen Liao or Yang He.

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Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The ethics committee of Peking University School and Hospital of Stomatology (PKUSSIRB-201837100) approved this study.

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Informed consent was obtained from all individual participants included in the study.

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The authors declare no competing interests.

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Jie, B., Han, B., Yao, B. et al. Automatic virtual reconstruction of maxillofacial bone defects assisted by ICP (iterative closest point) algorithm and normal people database. Clin Oral Invest 26, 2005–2014 (2022). https://doi.org/10.1007/s00784-021-04181-3

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  • DOI: https://doi.org/10.1007/s00784-021-04181-3

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