Clinical oral investigations · 2026
Deep learning-based automated assessment of alveolar bone loss in CBCT for periodontitis.
СтатьяНазвание и аннотация: Перевод в очереди
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Wang Yuyan, Zheng Hongjie, Duan Xinyu, Li Yihong, Jiang Jun, Tang Kunqi, Su Jiangling, Zhang Xiufang, Zheng Xiade, Zhan Xuan, Huang Xi
Аннотация
OBJECTIVES: This study aimed to develop and validate a CBCT-based automated system for assessing radiographic alveolar bone loss (RBL) to improve the accuracy and efficiency of periodontitis diagnosis.
METHODS: A total of 110 patients (2,796 teeth) with Stage I-IV periodontitis from four center were included. The nnU-Net framework was used to segment teeth, alveolar bone, and the cemento-enamel junction (CEJ). RBL was calculated automatically using an edge-constrained shortest path algorithm. The model was trained on data from Center A and externally validated with datasets from Centers B-D. Linear periodontal measurements from 11 CBCT scans were compared between manual and CAD-based segmentation. An independent validation set was used to assess automated staging accuracy and time efficiency.
RESULTS: The CAD system achieved Dice similarity coefficients (DSC) of 95.85% for teeth, 95.75% for alveolar bone, and 86.18% for the CEJ. External validation showed alveolar bone and tooth DSC values both above 95% and CEJ DSC values above 77%. Linear measurements showed strong agreement with manual segmentation (Spearman's ρ = 0.9187; ICC = 0.9266). For staging, the CAD system reached an overall accuracy of 87.31%, with 97.01% for Stage Ⅰ, 88.06% for Stage Ⅱ and 89.55% for Stage Ⅲ/Ⅳ. The CAD system represented a 13.42-fold acceleration compared with the manual workflow.
CONCLUSION: The CAD system enables accurate automated segmentation and RBL quantification on CBCT images, with robust multicenter performance and substantial gains in efficiency.
CLINICAL SIGNIFICANCE: This system offers a fast and reliable method for RBL assessment, supporting consistent diagnosis and monitoring of periodontitis.
METHODS: A total of 110 patients (2,796 teeth) with Stage I-IV periodontitis from four center were included. The nnU-Net framework was used to segment teeth, alveolar bone, and the cemento-enamel junction (CEJ). RBL was calculated automatically using an edge-constrained shortest path algorithm. The model was trained on data from Center A and externally validated with datasets from Centers B-D. Linear periodontal measurements from 11 CBCT scans were compared between manual and CAD-based segmentation. An independent validation set was used to assess automated staging accuracy and time efficiency.
RESULTS: The CAD system achieved Dice similarity coefficients (DSC) of 95.85% for teeth, 95.75% for alveolar bone, and 86.18% for the CEJ. External validation showed alveolar bone and tooth DSC values both above 95% and CEJ DSC values above 77%. Linear measurements showed strong agreement with manual segmentation (Spearman's ρ = 0.9187; ICC = 0.9266). For staging, the CAD system reached an overall accuracy of 87.31%, with 97.01% for Stage Ⅰ, 88.06% for Stage Ⅱ and 89.55% for Stage Ⅲ/Ⅳ. The CAD system represented a 13.42-fold acceleration compared with the manual workflow.
CONCLUSION: The CAD system enables accurate automated segmentation and RBL quantification on CBCT images, with robust multicenter performance and substantial gains in efficiency.
CLINICAL SIGNIFICANCE: This system offers a fast and reliable method for RBL assessment, supporting consistent diagnosis and monitoring of periodontitis.