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Related Experiment Video

Updated: May 27, 2025

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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Development and validation of a graph convolutional network (GCN)-based automatic superimposition method for

Yichen Pan, Zhechen Zhang, Tianmin Xu

    The Angle Orthodontist
    |February 17, 2025
    PubMed
    Summary

    A graph convolutional network (GCN) method accurately superimposes maxillary digital dental models (MDMs) for assessing adult tooth movement. This GCN approach demonstrates clinically acceptable errors and high reliability, comparable to manual methods.

    Keywords:
    Accuracy and reliabilityDigital dental modelGraph convolutional networkPalatal stable regionSuperimposition

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    Area of Science:

    • Orthodontics
    • Artificial Intelligence in Dentistry
    • 3D Imaging

    Background:

    • Accurate superimposition of maxillary digital dental models (MDMs) is crucial for assessing tooth movement in orthodontics.
    • Manual superimposition methods can be time-consuming and subject to inter-examiner variability.
    • Developing automated, reliable methods for MDM superimposition is an ongoing area of research.

    Purpose of the Study:

    • To validate a graph convolutional network (GCN)-based method for superimposing MDMs.
    • To compare the GCN method's accuracy and reliability against manual superimposition.
    • To quantify the clinical error associated with the GCN-based superimposition technique.

    Main Methods:

    • A GCN was trained on 100 3D MDMs, using manually annotated palatal stable structures for supervision.
    • Automatic segmentation of the palatal stable structure was performed, with accuracy assessed by Hausdorff distance.
    • Clinical error in tooth position and angulation was measured on first molars and central incisors.
    • Reliability was evaluated using intraclass correlation coefficients (ICCs).

    Main Results:

    • The average Hausdorff distance for palatal segmentation was 0.36 mm, exceeding intra- and inter-examiner deviations.
    • Tooth position deviation was less than 0.32 mm.
    • Tooth angulation differences were less than 0.26° for tip/torque and 0.46-0.61° for rotation.
    • ICCs for reliability ranged from 0.82 to 0.99.

    Conclusions:

    • The GCN-based MDM superimposition method is efficient for evaluating tooth movement in adults.
    • The method's clinical error in tooth position and angulation is acceptable for clinical use.
    • The GCN method exhibits high reliability, comparable to manual segmentation.