ArticleScientific reports2019
Automated detection of third molars and mandibular nerve by deep learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.
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Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it, 156 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
- The application of deep learning in intelligent assessment of horizontal bone loss in mandibular first molars.Clinical oral investigations · 2026Article
- 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
- Investigation of the Use of Artificial Intelligence in Anterior Loop Detection: A Panoramic Radiography Study.Diagnostics (Basel, Switzerland) · 2026Article
- Dental image analysis and surgical decision support using modified generative adversarial networks.BMC oral health · 2026Article
- A Deep Learning Model for Second-Molar Lesions Related to Impacted Third Molars.International dental journal · 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
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- Applications of artificial intelligence in tooth extraction: A systematic review.Journal of dental sciences · 2026Review
- Clinical decisions in Orthodontics using x-ray-based images and artificial intelligence approaches: a scoping review.Dental press journal of orthodontics · 2026Article
- A Novel Pipeline for Adrenal Gland Segmentation: Integration of a Hybrid Post-Processing Technique with Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Prognostic Evaluation of Lower Third Molar Eruption Status from Panoramic Radiographs Using Artificial Intelligence-Supported Machine and Deep Learning Models.Bioengineering (Basel, Switzerland) · 2025Article
- 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
- Automated quality evaluation of dental panoramic radiographs using deep learning.Imaging science in dentistry · 2025Article
- Evaluation of the mandibular canal and the third mandibular molar relationship by CBCT with a deep learning approach.Oral radiology · 2025Article
- 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
- Applications of Artificial Intelligence in Dental Medicine: A Critical Review.International dental journal · 2025Review
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The approximity of the inferior alveolar nerve (IAN) to the roots of lower third molars (M3) is a risk factor for the occurrence of nerve damage and subsequent sensory disturbances of the lower lip and chin following the removal of third molars. To assess this risk, the identification of M3 and IAN on dental panoramic radiographs (OPG) is mandatory. In this study, we developed and validated an automated approach, based on deep-learning, to detect and segment the M3 and IAN on OPGs. As a reference, M3s and IAN were segmented manually on 81 OPGs. A deep-learning approach based on U-net was applied on the reference data to train the convolutional neural network (CNN) in the detection and segmentation of the M3 and IAN. Subsequently, the trained U-net was applied onto the original OPGs to detect and segment both structures. Dice-coefficients were calculated to quantify the degree of similarity between the manually and automatically segmented M3s and IAN. The mean dice-coefficients for M3s and IAN were 0.947 ± 0.033 and 0.847 ± 0.099, respectively. Deep-learning is an encouraging approach to segment anatomical structures and later on in clinical decision making, though further enhancement of the algorithm is advised to improve the accuracy.
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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.