ArticleScientific reports2022
Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- Assessment of artificial intelligence in detecting errors on panoramic radiographs.Imaging science in dentistry · 2026Article
- The optimal diagnostic assistance system for predicting three-dimensional contact between mandibular third molars and the mandibular canal on panoramic radiographs.Oral radiology · 2026Article
- Buccal or palatal? AI-based localization of impacted maxillary canines using panoramic radiographs.BMC medical imaging · 2026Article
- Applications of artificial intelligence in tooth extraction: A systematic review.Journal of dental sciences · 2026Review
- 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
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
- Prognostic Evaluation of Lower Third Molar Eruption Status from Panoramic Radiographs Using Artificial Intelligence-Supported Machine and Deep Learning Models.Bioengineering (Basel, Switzerland) · 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
- Toward Precision Diagnosis of Maxillofacial Pathologies by Artificial Intelligence Algorithms: A Systematic Review.Journal of maxillofacial and oral surgery · 2025Review
- Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning.Scientific reports · 2025Article
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 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
- Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact.Scientific reports · 2025Article
- Preparing for downstream tasks in artificial intelligence for dental radiology: a baseline performance comparison of deep learning models.Dento maxillo facial radiology · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
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8 authors.
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Abstract
Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen's kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.
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