ArticleClinical oral investigations2024
Automatic detection and proximity quantification of inferior alveolar nerve and mandibular third molar on cone-beam computed tomography.
Article in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 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
- 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-enabled automatic segmentation of impacted mandibular third molars: A comprehensive comparison of multiple algorithms.Journal of dental sciences · 2026Article
- Automatic Detection and Proximity Quantification of Inferior Alveolar Nerve and Mandibular Third Molar on CBCT: A Systematic Review.Journal of pharmacy & bioallied sciences · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivesDuring mandibular third molar (MTM) extraction surgery, preoperative analysis to quantify the proximity of the MTM to the surrounding inferior alveolar nerve (IAN) is essential to minimize the risk of IAN injury. This study aims to propose an automated tool to quantitatively measure the proximity of IAN and MTM in cone-beam computed tomography (CBCT) images. MATERIALS AND
methodsUsing the dataset including 302 CBCT scans with 546 MTMs, a deep-learning-based network was developed to support the automatic detection of the IAN, MTM, and intersection region IR. To ensure accurate proximity detection, a distance detection algorithm and a volume measurement algorithm were also developed.
resultsThe deep learning-based model showed encouraging segmentation accuracy of the target structures (Dice similarity coefficient: 0.9531 ± 0.0145, IAN; 0.9832 ± 0.0055, MTM; 0.8336 ± 0.0746, IR). In addition, with the application of the developed algorithms, the distance between the IAN and MTM and the volume of the IR could be equivalently detected (90% confidence interval (CI): - 0.0345-0.0014 mm, distance; - 0.0155-0.0759 mm
conclusionsThis study presented a novel, fast, and accurate model for the detection and proximity quantification of the IAN and MTM on CBCT. CLINICAL RELEVANCE: This model illustrates that a deep learning network may assist surgeons in evaluating the risk of MTM extraction surgery by detecting the proximity of the IAN and MTM at a quantitative level that was previously unparalleled.
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Registered trials
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