ArticleInternational dental journal2025
Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations.
Article in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Artificial Intelligence Models in Oral and Maxillofacial Prosthesis Restoration: A Systematic Review.International dental journal · 2025Pooled it
- From Clinic to Community: An Interpretable Artificial Intelligence Framework for Enamel Caries Detection to Support Public Health Dentistry.European journal of dentistry · 2026Article
- Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning.International dental journal · 2026Article
- Integration of Artificial Intelligence in Designing Removable Partial Dentures.International dental journal · 2026Article
- Preliminary Evaluation of Large Language Models in Kennedy Classification and Removable Partial Denture Planning: An In Silico Study.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Dual Framework for Classification and Detection of Third Molar Impaction in Panoramic Radiographs.International dental journal · 2026Article
- Artificial Intelligence in Dentistry: A Concise Review of Reporting Checklists and Guidelines.International dental journal · 2026Review
- Multi-Regional deep learning models for identifying dental restorations and prosthesis in panoramic radiographs.BMC oral health · 2025Article
- Determination of Kennedy's classification in panoramic X-rays by automated tooth labeling.International journal of computer assisted radiology and surgery · 2025Article
- Correlation Between Type of Edentulism, Age, Socioeconomic Status and General Health.Journal of clinical medicine · 2025Article
- 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
- Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations.International dental journal · 2025Article
- OralHybridNet: A Deep Learning Framework for Multi-Label Classification of Dental Restorations and Prostheses in Panoramic Radiographs.Inquiry : a journal of medical care organization, provision and financingArticle
- Reliability of artificial intelligence algorithms in automated age estimation using orthopantomograms: A scoping review.Digital healthReview
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDental health is integral to overall well-being, with early detection of issues critical for prevention. This research work focuses on utilizing artificial intelligence and deep learning-based object detection techniques for automated detection of common dental issues in orthopantomography x-ray images, including broken roots, periodontally compromised teeth, and the Kennedy classification of partially edentulous arches.
methodsAn orthopantomography dataset has been used to train several models employing various object detection architectures, hyperparameters, and training techniques. The performance of these models was evaluated to select the one with the highest accuracy. This selected model was subsequently deployed for further testing and validation on unseen data to assess its real-world performance and potential for clinical application.
resultsThe proposed model not only facilitates the classification of the Kennedy classification but also offers detailed information about the arch (maxillary or mandibular) and specifies the affected side of the arch (right or left). It can diagnose multiple dental issues simultaneously within an image, enhancing diagnostic capabilities for dental practitioners.
conclusionsDespite a small dataset, satisfactory results were achieved through tailored hyperparameters and a piecewise annotation scheme.
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