Clinical oral investigations · 2026
Fully automatic AI diagnosis of alveolar bone defect in anterior teeth on CBCT images.
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Guo Runzhi, Ma Ning, Shi Tianlei, Wang Yufeng, Qin Qianyi, Chen Zining, Zuo Feifei, Wang Yajie, Li Weiran
Аннотация
OBJECTIVE: To develop a fully automatic artificial intelligence (AI) system for diagnosing alveolar bone defects (ABDs) in anterior teeth on cone-beam computed tomography (CBCT) images.
MATERIALS AND METHODS: This fully automatic AI system consists of two stages: (1) 2D image construction (sagittal and coronal slices) of anterior teeth root/bone morphology using a recognition algorithm, and (2) multi-perspective alveolar bone defect classification (normal, mild dehiscence, moderate dehiscence, severe dehiscence, and fenestration) based on the Hierarchical Multi-Scale Feature Fusion Network (HiFuse). In total, 300 CBCT images with 3600 anterior teeth from two clinical centers were used to train the model.
RESULTS: The system achieved automatic and accurate construction of sagittal and coronal slices for 12 anterior teeth, with a high structural similarity index measure (SSIM) index of 0.803. The HiFuse model significantly outperformed ConvNeXt and Swin Transformer counterparts (P < 0.05), achieving an accuracy of 0.936 (95% CI: 0.906-0.959), F1-score of 0.932 (95% CI: 0.895-0.955), recall of 0.936 (95% CI: 0.906-0.959), and precision of 0.928 (95% CI: 0.896-0.952). HiFuse also effectively distinguished between ABD types and accurately located alveolar bone defects.
CONCLUSIONS: Our proposed AI system demonstrated great performance in ABDs diagnosis of anterior teeth using original 3D CBCT images and has potential for assisting with orthodontic diagnosis and decision-making.
CLINICAL RELEVANCE: Accurate diagnosis of ABDs in anterior teeth is essential when selecting appropriate orthodontic treatment strategies and performing bone augmentation surgery. This AI system could preliminarily achieve diagnosis of ABDs in anterior teeth, reducing manual intervention and improving the overall diagnostic workflow.
MATERIALS AND METHODS: This fully automatic AI system consists of two stages: (1) 2D image construction (sagittal and coronal slices) of anterior teeth root/bone morphology using a recognition algorithm, and (2) multi-perspective alveolar bone defect classification (normal, mild dehiscence, moderate dehiscence, severe dehiscence, and fenestration) based on the Hierarchical Multi-Scale Feature Fusion Network (HiFuse). In total, 300 CBCT images with 3600 anterior teeth from two clinical centers were used to train the model.
RESULTS: The system achieved automatic and accurate construction of sagittal and coronal slices for 12 anterior teeth, with a high structural similarity index measure (SSIM) index of 0.803. The HiFuse model significantly outperformed ConvNeXt and Swin Transformer counterparts (P < 0.05), achieving an accuracy of 0.936 (95% CI: 0.906-0.959), F1-score of 0.932 (95% CI: 0.895-0.955), recall of 0.936 (95% CI: 0.906-0.959), and precision of 0.928 (95% CI: 0.896-0.952). HiFuse also effectively distinguished between ABD types and accurately located alveolar bone defects.
CONCLUSIONS: Our proposed AI system demonstrated great performance in ABDs diagnosis of anterior teeth using original 3D CBCT images and has potential for assisting with orthodontic diagnosis and decision-making.
CLINICAL RELEVANCE: Accurate diagnosis of ABDs in anterior teeth is essential when selecting appropriate orthodontic treatment strategies and performing bone augmentation surgery. This AI system could preliminarily achieve diagnosis of ABDs in anterior teeth, reducing manual intervention and improving the overall diagnostic workflow.