ArticleScientific reports2023
Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Artificial intelligence for assessment of the relationship between the mandibular canal and third molars using CBCT and panoramic radiography: a systematic review.BMC oral health · 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
- Automated classification of maxillary and mandibular dental views on intraoral photographs: a comparative benchmark study and mobile proof-of-concept.BMC oral health · 2026Article
- A Deep Learning Model for Second-Molar Lesions Related to Impacted Third Molars.International dental journal · 2026Article
- The Contemporary Role of Oral Maxillofacial Surgeons in Head and Neck Reconstructions.Journal of clinical medicine · 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
- A Deep Metric Learning and Multimodal Gated Fusion Framework for AI-Driven Risk Assessment of Lingual Plate Perforation and Mandibular Canal Injury in Posterior Mandible Implants.International journal of dentistry · 2026Article
- Applications of artificial intelligence in tooth extraction: A systematic review.Journal of dental sciences · 2026Review
- Automated assessment and detection of third molar and inferior alveolar nerve relations using UNet and transfer learning models.Scientific reports · 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
- Diagnostic performance of deep learning models in classifying mandibular third molar and mandibular canal contact status on panoramic radiographs: A systematic review and meta-analysis.Imaging science in dentistry · 2025Review
- 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
- Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.Clinical oral investigations · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve.IEEE journal of translational engineering in health and medicine · 2025Article
- Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists.Clinical oral investigations · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Evaluating the mandibular canal proximity is crucial for planning mandibular third molar extractions. Panoramic radiography is commonly used for radiological examinations before third molar extraction but has limitations in assessing the true contact relationship between the third molars and the mandibular canal. Therefore, the true relationship between the mandibular canal and molars can be determined only through additional cone-beam computed tomography (CBCT) imaging. In this study, we aimed to develop an automatic diagnosis method based on a deep learning model that can determine the true proximity between the mandibular canal and third molars using only panoramic radiographs. A total of 901 third molars shown on panoramic radiographs were examined with CBCT imaging to ascertain whether true proximity existed between the mandibular canal and the third molar by two radiologists (450 molars: true contact, 451 molars: true non-contact). Three deep learning models (RetinaNet, YOLOv3, and EfficientDet) were developed, with performance metrics of accuracy, sensitivity, and specificity. EfficientDet showed the highest performance, with an accuracy of 78.65%, sensitivity of 82.02%, and specificity of 75.28%. The proposed deep learning method can be helpful when clinicians must evaluate the proximity of the mandibular canal and a third molar using only panoramic radiographs without CBCT.
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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.