ArticleBMC oral health2025
Assessing the spatial relationship between mandibular third molars and the inferior alveolar canal using a deep learning-based approach: a proof-of-concept study.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.Oral radiology · 2026Pooled it
- Multimodal transformer-enhanced deep learning for accurate assessment of mandibular third molar-inferior alveolar canal contact using panoramic radiography and CBCT slices.BMC oral health · 2026Article
- Validation of artificial intelligence-assisted CBCT analysis for predicting inferior alveolar nerve proximity to impacted mandibular third molars: a diagnostic accuracy study.BMC oral health · 2026Article
- Artificial Intelligence in Predicting Surgical Problems and Postoperative Morbidity in Mandibular Third Molar Extractions.Annals of African medicine · 2026Article
- CBCT-based evaluation of mandibular third molar impaction and its effects on adjacent second molars in a Saudi population: a retrospective study of 420 scans.BMC oral health · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundThe distance between the mandibular third molar (M3) and the mandibular canal (MC) is a key factor in assessing the risk of injury to the inferior alveolar nerve (IAN). However, existing deep learning systems have not yet been able to accurately quantify the M3-MC distance in 3D space. The aim of this study was to develop and validate a deep learning-based system for accurate measurement of M3-MC spatial relationships in cone-beam computed tomography (CBCT) images and to evaluate its accuracy against conventional methods. MATERIALS AND
methodsWe propose an innovative approach for low-resource environments, using DeeplabV3 + for semantic segmentation of CBCT-extracted 2D images, followed by multi-category 3D reconstruction and visualization. Based on the reconstruction model, we applied the KD-Tree algorithm to measure the spatial minimum distance between M3 and MC. Through internal validation with randomly selected CBCT images, we compared the differences between the AI system, conventional measurement methods on the CBCT, and the gold standard measured by senior experts. Statistical analysis was performed using one-way ANOVA with Tukey HSD post-hoc tests (p < 0.05), employing multiple error metrics for comprehensive evaluation.
resultsOne-way ANOVA revealed significant differences among measurement methods. Subsequent Tukey HSD post-hoc tests showed significant differences between the AI reconstruction model and conventional methods. The measurement accuracy of the AI system compared to the gold standard was 0.19 for mean error (ME), 0.18 for mean absolute error (MAE), 0.69 for mean square error (MSE), 0.83 for root mean square error (RMSE), and 0.96 for coefficient of determination (R
conclusionsThese results indicate that the proposed AI system is highly accurate and reliable in M3-MC distance measurement and provides a powerful tool for preoperative risk assessment of M3 extraction.
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