ArticleScientific reports2020
Automatic mandibular canal detection using a deep convolutional neural network.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05350228 (Accuracy of Computer-aided Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT Images Using Deep Learning Model), which is not on this map. Cited by 60 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Accuracy of Computer-aided Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study.
Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it, 147 citations in OpenAlex.
- 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
- Enhanced Tooth Region Detection Using Pretrained Deep Learning Models.International journal of environmental research and public health · 2022Trial
- Review
- Artificial Intelligence in Plastic and Reconstructive Surgery: A Bibliometric Study of the 50 Most-cited Articles.Plastic and reconstructive surgery. Global open · 2026Article
- 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
- Artificial Intelligence for Diagnosing Normal Anatomical Variants and Pathological Oral Mucosal Lesions: A Prospective Observational Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Automated prediction of hand-wrist maturation stage from lateral cephalometric and panoramic radiographs using a deep learning approach.European journal of orthodontics · 2026Article
- Investigation of the Use of Artificial Intelligence in Anterior Loop Detection: A Panoramic Radiography Study.Diagnostics (Basel, Switzerland) · 2026Article
- A customised 3D U-Net model for bifid mandibular canal segmentation and detection on CBCT: a diagnostic accuracy study.BMC oral health · 2026Article
- Comparison of Artificial Intelligence Models for Automatic Segmentation of the Mandibular Canals and Branches.International dental journal · 2026Article
- Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.Scientific data · 2026Article
- Clinically applicable deep learning model for segmentation of the mandibular bone and inferior alveolar canal in CBCT cross-sectional images.BMC oral health · 2026Article
- Application of deep learning in evaluating the anatomical relationship between the mandibular third molar and inferior alveolar nerve: A scoping review.Medicina oral, patologia oral y cirugia bucal · 2026Article
- Comparison of Conventional Root Tip Resection with Digitally Guided Resection-An In Vitro Study.Dentistry journal · 2025Article
- Deploying a novel deep learning framework for segmentation of specific anatomical structures on cone-beam CT.Oral radiology · 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
- A Comparison of Deep Learning vs. Dental Implantologists in Cone-Beam Computed Tomography-Based Bone Quality Classification.Journal of imaging informatics in medicine · 2025Article
- Automatic Segmentation of the Infraorbital Canal in CBCT Images: Anatomical Structure Recognition Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Segmentation of the nasopalatine canal and detection of canal furcation status with artificial intelligence on cone-beam computed tomography images.Oral radiology · 2025Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients' discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.