ArticleClinical oral investigations2022
Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT.
Article in Clinical oral investigations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.Oral radiology · 2026Pooled it
- The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- A Deep Learning Model for Second-Molar Lesions Related to Impacted Third Molars.International dental journal · 2026Article
- A phantom study on the evaluation of the YOLOv8 deep learning model in the detection of tooth ankylosis on cone-beam computed tomography images.Head & face medicine · 2026Article
- A customised 3D U-Net model for bifid mandibular canal segmentation and detection on CBCT: a diagnostic accuracy study.BMC oral health · 2026Article
- A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.Scientific reports · 2026Article
- Deep learning-based identification and maturation assessment of the zygomaticomaxillary suture in cone-beam computed tomography images.Progress in orthodontics · 2026Article
- Artificial intelligence-enabled automatic segmentation of impacted mandibular third molars: A comprehensive comparison of multiple algorithms.Journal of dental sciences · 2026Article
- Using swin UNETR deep model for automated detection of alveolar bone fenestration/dehiscence in CBCT.Frontiers in bioengineering and biotechnology · 2026Article
- A deep learning framework for automated dental segmentation and diagnostic report generation from cone-beam computed tomography.Head & face medicine · 2025Article
- Assessing the spatial relationship between mandibular third molars and the inferior alveolar canal using a deep learning-based approach: a proof-of-concept study.BMC oral health · 2025Article
- AI-powered segmentation of bifid mandibular canals using CBCT.BMC oral health · 2025Article
- Evaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography.Oral radiology · 2025Article
- Evaluation of the mandibular canal and the third mandibular molar relationship by CBCT with a deep learning approach.Oral radiology · 2025Article
- Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.Clinical oral investigations · 2025Article
- A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs.Journal of imaging informatics in medicine · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Automatic detection and proximity quantification of inferior alveolar nerve and mandibular third molar on cone-beam computed tomography.Clinical oral investigations · 2024Article
- A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.BMC oral health · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
objectivesThe objective of our study was to develop and validate a deep learning approach based on convolutional neural networks (CNNs) for automatic detection of the mandibular third molar (M3) and the mandibular canal (MC) and evaluation of the relationship between them on CBCT. MATERIALS AND
methodsA dataset of 254 CBCT scans with annotations by radiologists was used for the training, the validation, and the test. The proposed approach consisted of two modules: (1) detection and pixel-wise segmentation of M3 and MC based on U-Nets; (2) M3-MC relation classification based on ResNet-34. The performances were evaluated with the test set. The classification performance of our approach was compared with two residents in oral and maxillofacial radiology.
resultsFor segmentation performance, the M3 had a mean Dice similarity coefficient (mDSC) of 0.9730 and a mean intersection over union (mIoU) of 0.9606; the MC had a mDSC of 0.9248 and a mIoU of 0.9003. The classification models achieved a mean sensitivity of 90.2%, a mean specificity of 95.0%, and a mean accuracy of 93.3%, which was on par with the residents.
conclusionsOur approach based on CNNs demonstrated an encouraging performance for the automatic detection and evaluation of the M3 and MC on CBCT. Clinical relevance An automated approach based on CNNs for detection and evaluation of M3 and MC on CBCT has been established, which can be utilized to improve diagnostic efficiency and facilitate the precision diagnosis and treatment of M3.
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