American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2026
Clinically oriented deep learning system integrating linear and morphological assessment for external orthodontic root resorption.
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Yang Dan, Yu Shuangjiang, Zhao Yue, Hu Yao, Song Jinlin, Zheng Leilei, Liu Yang
Аннотация
INTRODUCTION: Manual linear assessment, a classic method for evaluating orthodontic external root resorption (OERR), has limitations: unreliable dento-osseous junction identification and operator variability, time-consuming measurements, and inability to capture 3-dimensional (3D) morphologic changes.
METHODS: We developed OERR-Net, a deep learning system for objective, real-time OERR linear assessment and 3D visualization using pre and postorthodontic treatment cone-beam computed tomography scans. First, leveraging Transformer architecture, inspired by ChatGPT (OpenAI, San Francisco, Calif), the Swin-UNETR model was adopted for apex-aware tooth segmentation and 3D reconstruction. Second, a novel algorithm (ToothLM) was proposed for automatic tooth length measurement. Third, the system achieved simultaneous grading and 3D morphologic visualization. An end-to-end validation workflow was established, covering segmentation to grading, with Swin-UNETR's superiority demonstrated through qualitative, saliency, and quantitative analyses. Length accuracy was validated via difference and the Bland-Altman analyses, and grading performance was compared with orthodontists' evaluations.
RESULTS: The study included 100 paired cone-beam computed tomography scans (1560 incisors). First, Swin-UNETR outperformed U-Net and UNETR, achieving the highest agreement with the ground truth (dice similarity score = 90.98%). Second, ToothLM showed excellent agreement with expert manual measurements (intraclass correlation coefficient = 0.999). Finally, OERR-Net achieved superior grading accuracy (maxillary incisors: 97.37% vs 74.34%; mandibular incisors: 96.82% vs 94.27%) than the subjective assessments by orthodontists, captured subtle morphologic changes, and reduced subjective assessment time by 50%, enhancing efficiency and accuracy.
CONCLUSIONS: The proposed automatic OERR assessment system aligns with classic practices, clarifies resorption patterns, and helps treatment selection based on severity. Current validation is single-center; broader applicability requires future validation.
METHODS: We developed OERR-Net, a deep learning system for objective, real-time OERR linear assessment and 3D visualization using pre and postorthodontic treatment cone-beam computed tomography scans. First, leveraging Transformer architecture, inspired by ChatGPT (OpenAI, San Francisco, Calif), the Swin-UNETR model was adopted for apex-aware tooth segmentation and 3D reconstruction. Second, a novel algorithm (ToothLM) was proposed for automatic tooth length measurement. Third, the system achieved simultaneous grading and 3D morphologic visualization. An end-to-end validation workflow was established, covering segmentation to grading, with Swin-UNETR's superiority demonstrated through qualitative, saliency, and quantitative analyses. Length accuracy was validated via difference and the Bland-Altman analyses, and grading performance was compared with orthodontists' evaluations.
RESULTS: The study included 100 paired cone-beam computed tomography scans (1560 incisors). First, Swin-UNETR outperformed U-Net and UNETR, achieving the highest agreement with the ground truth (dice similarity score = 90.98%). Second, ToothLM showed excellent agreement with expert manual measurements (intraclass correlation coefficient = 0.999). Finally, OERR-Net achieved superior grading accuracy (maxillary incisors: 97.37% vs 74.34%; mandibular incisors: 96.82% vs 94.27%) than the subjective assessments by orthodontists, captured subtle morphologic changes, and reduced subjective assessment time by 50%, enhancing efficiency and accuracy.
CONCLUSIONS: The proposed automatic OERR assessment system aligns with classic practices, clarifies resorption patterns, and helps treatment selection based on severity. Current validation is single-center; broader applicability requires future validation.