ArticleLife (Basel, Switzerland)2023
Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs.
Article in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
21 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
- Comparative Evaluation of Hybrid Attention-CNN and Vision Transformer Models for Multi-Class Classification of Third-Second Molar Relationships on CBCT.Diagnostics (Basel, Switzerland) · 2026Article
- Radiolabeled Vitamins and Nanosystems as Potential Agents in Oncology Theranostics: Developed Approaches and Future Perspectives.Journal of personalized medicine · 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
- Deep learning-based approach to third molar impaction analysis with clinical classifications.Scientific reports · 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
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- 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
- Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis.International dental journal · 2025Article
- Indirect-Oscillation Sign Suggesting Infective Endocarditis on the Routine Chest CT.Journal of cardiovascular development and disease · 2024Article
- Theranostic Approaches for Gastric Cancer: An Overview of In Vitro and In Vivo Investigations.Cancers · 2024Review
- Artificial Intelligence Application in a Case of Mandibular Third Molar Impaction: A Systematic Review of the Literature.Journal of clinical medicine · 2024Review
- A Review of Advances in Molecular Imaging of Rheumatoid Arthritis: From In Vitro to Clinic Applications Using Radiolabeled Targeting Vectors with Technetium-99m.Life (Basel, Switzerland) · 2024Review
- Development and Validation of an Ultrasonography-Based Machine Learning Model for Predicting Outcomes of Bruxism Treatments.Diagnostics (Basel, Switzerland) · 2024Article
- Automated classification of mandibular canal in relation to third molar using CBCT images.F1000Research · 2024Article
- Challenges in Pedicatric Orthodontics and Radiology: Evidence from Recent Studies and Implications for Clinical Practice: A Narrative Review.Galen medical journal · 2024Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this investigation was to evaluate the diagnostic performance of two convolutional neural networks (CNNs), namely ResNet-152 and VGG-19, in analyzing, on panoramic images, the rapport that exists between the lower third molar (MM3) and the mandibular canal (MC), and to compare this performance with that of an inexperienced observer (a sixth year dental student). Utilizing the k-fold cross-validation technique, 142 MM3 images, cropped from 83 panoramic images, were split into 80% as training and validation data and 20% as test data. They were subsequently labeled by an experienced radiologist as the gold standard. In order to compare the diagnostic capabilities of CNN algorithms and the inexperienced observer, the diagnostic accuracy, sensitivity, specificity, and positive predictive value (PPV) were determined. ResNet-152 achieved a mean sensitivity, specificity, PPV, and accuracy, of 84.09%, 94.11%, 92.11%, and 88.86%, respectively. VGG-19 achieved 71.82%, 93.33%, 92.26%, and 85.28% regarding the aforementioned characteristics. The dental student's diagnostic performance was respectively 69.60%, 53.00%, 64.85%, and 62.53%. This work demonstrated the potential use of deep CNN architecture for the identification and evaluation of the contact between MM3 and MC in panoramic pictures. In addition, CNNs could be a useful tool to assist inexperienced observers in more accurately identifying contact relationships between MM3 and MC on panoramic images.
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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.