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Related Concept Videos

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Related Experiment Video

Updated: Sep 15, 2025

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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ToothAxis: Generalizable Tooth Axis Estimation Network From CBCT or IOS Models.

Nan Bao, Qingyao Luo, Jiamin Wu

    IEEE Journal of Biomedical and Health Informatics
    |July 17, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces ToothAxis, a novel AI network for accurately estimating tooth axes from 3D dental scans. The system improves precision in dental procedures like orthodontics and implants by automating this crucial measurement.

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    Area of Science:

    • Biomedical Engineering
    • Computer Vision
    • Dental Imaging

    Background:

    • Accurate tooth axis estimation is vital for dental applications like orthodontics and implants.
    • Current methods rely on manual annotation, which is time-consuming and prone to errors.
    • Digital 3D dental models from Cone-beam computed tomography (CBCT) and intraoral scanning (IOS) are standard but have limitations for axis detection.

    Purpose of the Study:

    • To develop an automated, accurate method for estimating tooth axes from 3D dental models.
    • To address the challenge of incomplete root information in intraoral scans.
    • To improve the efficiency and reliability of tooth axis measurement in clinical settings.

    Main Methods:

    • A novel two-stage deep learning network, ToothAxis, was proposed.
    • Stage 1: Implicit-function based 3D tooth completion for intraoral scan data.
    • Stage 2: Point-wise offset-based module for tooth axis estimation, enhanced with class-specific feature attention.

    Main Results:

    • ToothAxis achieved low angle errors for landmark axes (LA), posterior sagittal axes (PSA), and lateral sagittal axes (LSA) on CBCT data (LA: 2.921°, PSA: 4.801°, LSA: 5.074°).
    • Comparable accuracy was obtained for IOS data (LA: 5.326°, PSA: 6.360°, LSA: 6.520°).
    • The method demonstrated superior performance compared to existing state-of-the-art approaches.

    Conclusions:

    • The ToothAxis network provides accurate and automated tooth axis estimation from both CBCT and IOS dental models.
    • The two-stage approach effectively handles data limitations, particularly the absence of root information in IOS.
    • This automated method has the potential to significantly benefit orthodontic and dental implant procedures.