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Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

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Clinically oriented deep learning system integrating linear and morphological assessment for external orthodontic

Dan Yang¹, Shuangjiang Yu², Yue Zhao²

¹Department of Orthodontics, The Affiliated Stomatological Hospital of Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Oral Diseases, Chongqing, China; Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China.

American Journal of Orthodontics and Dentofacial Orthopedics : Official Publication of the American Association of Orthodontists, Its Constituent Societies, and the American Board of Orthodontics

|May 26, 2026

Summary

This study introduces OERR-Net, an AI system for assessing orthodontic external root resorption (OERR) using 3D imaging. OERR-Net offers objective, efficient, and accurate OERR grading and visualization, improving upon manual methods.

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Area of Science:

Orthodontics
Artificial Intelligence in Dentistry
Medical Imaging Analysis

Background:

Manual assessment of orthodontic external root resorption (OERR) is subjective and time-consuming.
Traditional methods struggle with accurate dento-osseous junction identification and capturing 3D morphologic changes.

Purpose of the Study:

To develop an automated deep learning system (OERR-Net) for objective, real-time OERR assessment and 3D visualization.
To improve the accuracy and efficiency of OERR evaluation compared to manual methods.

Main Methods:

Utilized Swin-UNETR for apex-aware tooth segmentation and 3D reconstruction from cone-beam computed tomography (CBCT) scans.
Developed a novel ToothLM algorithm for automatic tooth length measurement.
Integrated simultaneous OERR grading and 3D morphologic visualization within the OERR-Net system.

Main Results:

Swin-UNETR demonstrated superior segmentation performance (Dice score = 90.98%).
ToothLM achieved excellent agreement with expert measurements (ICC = 0.999).
OERR-Net significantly improved grading accuracy (up to 97.37%) and reduced assessment time by 50%.

Conclusions:

OERR-Net provides an objective, efficient, and accurate method for OERR assessment, complementing traditional techniques.
The system aids in clarifying resorption patterns and informing treatment selection based on severity.
Future multi-center validation is recommended to assess broader applicability.