ArticleScientific reports2022
Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in 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 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.
- 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
- 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
- Automated assessment and detection of third molar and inferior alveolar nerve relations using UNet and transfer learning models.Scientific reports · 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
- 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
- Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them.Scientific reports · 2024Article
- Post-Traumatic Trigeminal Neuropathic Pain: A Narrative Review of Understanding, Management, and Prognosis.Biomedicines · 2024Review
- The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs.The Saudi dental journal · 2024Review
- Automated classification of mandibular canal in relation to third molar using CBCT images.F1000Research · 2024Article
- Imaging in Third Molar Surgery: A Clinical Update.Journal of clinical medicine · 2023Review
- Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning.Scientific reports · 2023Article
- Preinterventional Third-Molar Assessment Using Robust Machine Learning.Journal of dental research · 2023Article
- Deep learning model for the automated evaluation of contact between the lower third molar and inferior alveolar nerve on panoramic radiography.Journal of dental sciences · 2023Article
- Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies.Diagnostics (Basel, Switzerland) · 2023Article
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10 authors.
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Abstract
In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results indicated that ResNet50v2 using SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860 and 0.890 for the contact analyses and 0.766 and 0.843 for the continuity analyses. In the contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance.
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