ArticleJournal of dental sciences2023
Deep learning model for the automated evaluation of contact between the lower third molar and inferior alveolar nerve on panoramic radiography.
Article in Journal of dental sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- 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
- Effectiveness of Artificial Intelligence in the Automatic Diagnosis of Lesions Detected in Panoramic Radiographs.Journal of imaging informatics in 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
- 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
- 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
- Development of an AI-Supported Clinical Tool for Assessing Mandibular Third Molar Tooth Extraction Difficulty Using Panoramic Radiographs and YOLO11 Sub-Models.Diagnostics (Basel, Switzerland) · 2025Article
- Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact.Scientific reports · 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
- 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
- Optimal Training Positive Sample Size Determination for Deep Learning with a Validation on CBCT Image Caries Recognition.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence Application in a Case of Mandibular Third Molar Impaction: A Systematic Review of the Literature.Journal of clinical medicine · 2024Review
- The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs.The Saudi dental journal · 2024Review
- Persistent paresthesia of inferior alveolar nerve after accidental extrusion of calcium hydroxide paste containing iodoform into the mandibular canal.Journal of dental sciences · 2024Article
- Automated classification of mandibular canal in relation to third molar using CBCT images.F1000Research · 2024Article
- Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks.Medicina (Kaunas, Lithuania) · 2023Article
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6 authors.
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Abstract
Background/purpose: In lower third molar (LM3) surgery, panoramic radiography (PAN) is important for the initial assessment of the anatomical association between LM3 and the inferior alveolar nerve (IAN). This study aimed to develop a deep learning model for the automated evaluation of the LM3-IAN association on PAN. Further, its performance was compared with that of oral surgeons using original and external datasets. Materials and methods: In total, 579 panoramic images of LM3 from 384 patients in the original dataset were utilized. The images were divided into 483 images for the training dataset and 96 for the testing dataset at a ratio of 83:17. The external dataset comprising 58 images from an independent institution was used for testing only. The LM3-IAN associations on PAN were categorized into direct or indirect contact based on cone-beam computed tomography (CBCT). The You Only Look Once (YOLO) version 3 algorithm, a fast object detection system, was applied. To increase the amount of training data for deep learning, PAN images were augmented using the rotation and flip techniques. Results: The final YOLO model had high accuracy (0.894 in the original dataset and 0.927 in the external dataset), recall (0.925, 0.919), precision (0.891, 0.971), and f1-score (0.908, 0.944). Meanwhile, oral surgeons had lower accuracy (0.628, 0.615), recall (0.821, 0.497), precision (0.607, 0.876), and f1-score (0.698, 0.634). Conclusion: The YOLO-driven deep learning model can help oral surgeons in the decision-making process of applying additional CBCT to confirm the LM3-IAN association based on PAN images.
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