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Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning

  • Original Article
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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Orthognathic surgery requires an accurate surgical plan of how bony segments are moved and how the face passively responds to the bony movement. Currently, finite element method (FEM) is the standard for predicting facial deformation. Deep learning models have recently been used to approximate FEM because of their faster simulation speed. However, current solutions are not compatible with detailed facial meshes and often do not explicitly provide the network with known boundary type information. Therefore, the purpose of this proof-of-concept study is to develop a biomechanics-informed deep neural network that accepts point cloud data and explicit boundary types as inputs to the network for fast prediction of soft-tissue deformation.

Methods

A deep learning network was developed based on the PointNet++ architecture. The network accepts the starting facial mesh, input displacement, and explicit boundary type information and predicts the final facial mesh deformation.

Results

We trained and tested our deep learning model on datasets created from FEM simulations of facial meshes. Our model achieved a mean error between 0.159 and 0.642 mm on five subjects. Including explicit boundary types had mixed results, improving performance in simulations with large deformations but decreasing performance in simulations with small deformations. These results suggest that including explicit boundary types may not be necessary to improve network performance.

Conclusion

Our deep learning method can approximate FEM for facial change prediction in orthognathic surgical planning by accepting geometrically detailed meshes and explicit boundary types while significantly reducing simulation time.

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Data availability

Data are not publicly available.

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Funding

This work was supported in part by NIH grants (R01 DE022676, R01 DE027251 and R01 DE021863).

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Author notes

  1. Nathan Lampen and Daeseung Kim have contributed equally to this work.

Authors and Affiliations

  1. Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Nathan Lampen, Xi Fang, Xuanang Xu & Pingkun Yan

  2. Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, 77030, USA

    Daeseung Kim, Tianshu Kuang, Hannah H. Deng, Joshua C. Barber, Jamie Gateno & James Xia

  3. Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, NY, 10021, USA

    Jamie Gateno & James Xia

Authors
  1. Nathan Lampen
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  2. Daeseung Kim
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  3. Xi Fang
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  4. Xuanang Xu
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  7. Joshua C. Barber
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  8. Jamie Gateno
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  9. James Xia
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  10. Pingkun Yan
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Contributions

NL, DK, XF, XX, JX, and PY conceived the study and designed the methods. DK and NL generated and prepared the data. The first draft of the manuscript was written by NL and DK. All authors commented on previous versions of the manuscript, read and approved the final manuscript.

Corresponding authors

Correspondence to James Xia or Pingkun Yan.

Ethics declarations

Conflict of interest

The authors Nathan Lampen, Daeseung Kim, Xi Fang, Xuanang Xu, Tianshu Kuang, Hannah H. Deng, Joshua C. Barber, Jamie Gateno, James Xia, and Pingkun Yan declare that they have no conflicts of interest.

Ethical approval

The study was approved by our Institutional Review Board under IRB#: Pro00008890.

Informed consent

Informed consent was obtained for all subjects under IRB#: Pro00008890.

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Cite this article

Lampen, N., Kim, D., Fang, X. et al. Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning. Int J CARS 17, 945–952 (2022). https://doi.org/10.1007/s11548-022-02596-1

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  • DOI: https://doi.org/10.1007/s11548-022-02596-1

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