ArticleBMC oral health2022
A two-stage deep learning architecture for radiographic staging of periodontal bone loss.
Article in BMC oral health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 4 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 4 syntheses or guidelines pooled it, 112 citations in OpenAlex.
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis.Dento maxillo facial radiology · 2025Pooled it
- Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis.BMC oral health · 2023Pooled it
- The application of deep learning in intelligent assessment of horizontal bone loss in mandibular first molars.Clinical oral investigations · 2026Article
- From Panoramic Radiographs to AI-Assisted Radiographic Periodontal Charting: Current Evidence and Future Perspectives.Dentistry journal · 2026Review
- Deep Learning for the Assessment of Alveolar Bone Loss on Intraoral Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Global Attention Mechanism-Enhanced Deep Learning for Multi-Sequence MRI Diagnosis of Early Femoral Head Osteonecrosis.Annals of the New York Academy of Sciences · 2026Article
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- Integration of Artificial Intelligence in Designing Removable Partial Dentures.International dental journal · 2026Article
- Deep learning-based automated assessment of alveolar bone loss in CBCT for periodontitis.Clinical oral investigations · 2026Article
- Automated periodontitis diagnosis and staging using an end-to-end deep learning model on panoramic dental radiographs.Oral radiology · 2026Article
- Accuracy of Deep Learning Models in Detecting Mandibular Furcation Defects on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- A Lightweight and Explainable AI Framework Toward Automated Infraocclusion Detection in Pediatric Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Fractal Analysis and Artificial Intelligence for Radiographic Detection of Periodontal Bone Loss: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- OralSegNet: An Approach to Early Detection of Oral Disease Using Transfer Learning.Oral diseases · 2026Article
- Automated deep learning detection of orthodontically induced external apical root resorption in maxillary incisors on panoramic radiographs.Progress in orthodontics · 2026Article
- The impact of artificial intelligence on periodontal disease detection and treatment.Frontiers in dental medicine · 2026Review
- Machine learning assisted classification of periodontal health and periodontitis using alveolar bone loss measurements on bitewing radiographs.Frontiers in oral health · 2026Article
- The Application and Performance of Artificial Intelligence (AI) Models in the Diagnosis, Classification, and Prediction of Periodontal Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiographic periodontal bone loss is one of the most important basis for periodontitis staging, with problems such as limited accuracy, inconsistency, and low efficiency in imaging diagnosis. Deep learning network may be a solution to improve the accuracy and efficiency of periodontitis imaging staging diagnosis. This study aims to establish a comprehensive and accurate radiological staging model of periodontal alveolar bone loss based on panoramic images.
methodsA total of 640 panoramic images were included, and 3 experienced periodontal physicians marked the key points needed to calculate the degree of periodontal alveolar bone loss and the specific location and shape of the alveolar bone loss. A two-stage deep learning architecture based on UNet and YOLO-v4 was proposed to localize the tooth and key points, so that the percentage of periodontal alveolar bone loss was accurately calculated and periodontitis was staged. The ability of the model to recognize these features was evaluated and compared with that of general dental practitioners.
resultsThe overall classification accuracy of the model was 0.77, and the performance of the model varied for different tooth positions and categories; model classification was generally more accurate than that of general practitioners.
conclusionsIt is feasible to establish deep learning model for assessment and staging radiographic periodontal alveolar bone loss using two-stage architecture based on UNet and YOLO-v4.
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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.