ArticleScientific reports2023
Teeth and prostheses detection in dental panoramic X-rays using CNN-based object detector and a priori knowledge-based algorithm.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 30 citations in OpenAlex.
- Tooth Detection and Numbering Performance of Two AI-Based Systems on Panoramic Radiographs: A Comparative Study.Dentistry journal · 2026Article
- An augmentation-enhanced transformer model for forensic identification using panoramic dental x-rays.International journal of legal medicine · 2026Article
- Structure-Guided Tooth Numbering and Lesion Localization in Visible Light Oral Images.Journal of imaging · 2026Article
- The Influence of Panoramic Radiograph Quality on the Accuracy of AI-Based Tooth Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Hybrid Faster R-CNN for Tooth Numbering in Periapical Radiographs Based on Fédération Dentaire Internationale System.Diagnostics (Basel, Switzerland) · 2025Article
- Decision-Support for Restorative Dentistry: Hybrid Optimization Enhances Detection on Panoramic Radiographs.Healthcare (Basel, Switzerland) · 2025Article
- Multi-Regional deep learning models for identifying dental restorations and prosthesis in panoramic radiographs.BMC oral health · 2025Article
- An end-to-end deep-learning system for segmentation and classification of dental caries from radiovisiography images.Journal of conservative dentistry and endodontics · 2025Article
- Detection and dual-label classification of tooth number and condition in dental panoramic radiographs including deciduous teeth.Radiological physics and technology · 2025Article
- A visualization system for intelligent diagnosis and statistical analysis of oral diseases based on panoramic radiography.Scientific reports · 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
- Assessment of using transfer learning with different classifiers in hypodontia diagnosis.BMC oral health · 2025Article
- A dual-labeled dataset and fusion model for automatic teeth segmentation, numbering, and state assessment on panoramic radiographs.BMC oral health · 2024Article
- Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Deep learning techniques for automatically detecting teeth in dental X-rays have gained popularity, providing valuable assistance to healthcare professionals. However, teeth detection in X-ray images is often hindered by alterations in tooth appearance caused by dental prostheses. To address this challenge, our paper proposes a novel method for teeth detection and numbering in dental panoramic X-rays, leveraging two separate CNN-based object detectors, namely YOLOv7, for detecting teeth and prostheses, alongside an optimization algorithm to refine the outcomes. The study utilizes a dataset of 3138 radiographs, of which 2553 images contain prostheses, to build a robust model. The tooth and prosthesis detection algorithms perform excellently, achieving mean average precisions of 0.982 and 0.983, respectively. Additionally, the trained tooth detection model is verified using an external dataset, and six-fold cross-validation is conducted to demonstrate the proposed method's feasibility and robustness. Moreover, the investigation of performance improvement resulting from the inclusion of prosthesis information in the teeth detection process reveals a marginal increase in the average F1-score, rising from 0.985 to 0.987 compared to the sole teeth detection method. The proposed method is unique in its approach to numbering teeth as it incorporates prosthesis information and considers complete restorations such as dental implants and dentures of fixed bridges during the teeth enumeration process, which follows the universal tooth numbering system. These advancements hold promise for automating dental charting processes.
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