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Deep learning for virtual orthodontic bracket removal: tool establishment and application

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Abstract   

Objective

We aimed to develop a tool for virtual orthodontic bracket removal based on deep learning algorithms for feature extraction from bonded teeth and to demonstrate its application in a bracket position assessment scenario.

Materials and methods

Our segmentation network for virtual bracket removal was trained using dataset A, containing 978 bonded teeth, 20 original teeth, and 20 brackets generated by scanners. The accuracy and segmentation time of the network were tested by dataset B, which included an additional 118 bonded teeth without knowing the original tooth morphology. This tool was then applied for bracket position assessment. The clinical crown center, bracket center, and orientations of separated teeth and brackets were extracted for analyzing the linear distribution and angular deviation of bonded brackets.

Results

This tool performed virtual bracket removal in 2.9 ms per tooth with accuracies of 98.93% and 97.42% (P < 0.01) in datasets A and B, respectively. The tooth surface and bracket characteristics were extracted and used to evaluate the results of manually bonded brackets by 49 orthodontists. Personal preferences for bracket angulation and bracket distribution were displayed graphically and tabularly.

Conclusions

The tool's efficiency and precision are satisfactory, and it can be operated without original tooth data. It can be used to display the bonding deviation in the bracket position assessment scenario.

Clinical significance

With the aid of this tool, unnecessary bracket removal can be avoided when evaluating bracket positions and modifying treatment plans. It has the potential to produce retainers and orthodontic devices prior to tooth debonding.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

This study was funded by the Science and Technology Commission of Shanghai Municipality [Grand numbers 19441906200 and 18441903600], Clinical Research Plan of SHDC [Grand number SHDC2020CR3009A], Shanghai Rising-Star Program [Grand number 19QA1405200], Innovative Research Team of High-Level Local Universities in Shanghai [Grand number SSMU-ZDCX20180902], Young Doctor Collaborative Innovation Team of Ninth People's Hospital Affiliated to Shanghai Jiao Tong University, School of Medicine [Grand number QC2018-02], Innovative Research Team of High-Level Local Universities in Shanghai [SHSMU-ZLCX20212402] and the CSA Clinical Research Fund [CSA-O2020-04] for financial support. Cross Disciplinary Research Fund from Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine [JYJC202130].

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Authors and Affiliations

  1. Department of Orthodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China

    Ruomei Li, Cheng Zhu, Fengting Chu, Quan Yu, Ningjuan Ouyang, Yu Jin, Weiming Guo, Lunguo Xia, Qiping Feng & Bing Fang

  2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Di Fan

Authors
  1. Ruomei Li
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  2. Cheng Zhu
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  3. Fengting Chu
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  4. Quan Yu
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  5. Di Fan
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  6. Ningjuan Ouyang
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  7. Yu Jin
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  9. Lunguo Xia
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  11. Bing Fang
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Contributions

LR contributed to conceptualization, methodology, formal analysis, investigation, writing—original draft, and visualization. ZC contributed to validation, formal analysis, investigation, data curation, writing—review & editing, and visualization. CF contributed to conceptualization, methodology, validation, investigation, resources, and writing—review & editing.

YQ contributed to methodology, validation, resources, data curation, and visualization. FD contributed to methodology, software, writing—review & editing, and validation. ON contributed to investigation, and writing—review & editing. JY contributed to investigation and writing—review & editing. GW contributed to investigation and writing—review & editing. XL contributed to validation, writing—review & editing, and supervision. FQ contributed to validation, writing—review & editing, supervision, and project administration.

FB contributed to validation, writing—review & editing, supervision, and funding acquisition.

Corresponding authors

Correspondence to Lunguo Xia, Qiping Feng or Bing Fang.

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Ethics approval

The study was approved by the Independent Ethics Committee of the Shanghai Ninth People’s Hospital affiliated twith Shanghai Jiao Tong University, School of Medicine (SH9H-2021-T360-1).

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The authors declare no competing interests.

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The authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article.

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Ruomei Li and Cheng Zhu joint first authors.

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Li, R., Zhu, C., Chu, F. et al. Deep learning for virtual orthodontic bracket removal: tool establishment and application. Clin Oral Invest 28, 121 (2024). https://doi.org/10.1007/s00784-023-05440-1

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