ArticleDiagnostics (Basel, Switzerland)2023
Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.Odontology · 2026Pooled it
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review.Dento maxillo facial radiology · 2025Pooled it
- Bridging the Implementation Gap: Artificial Intelligence in Periodontology from Proof-of-Concept to Clinical Governance-A Systematic Scoping Review with Evidence from Kazakhstan.Dentistry journal · 2026Review
- Agreement between artificial intelligence and periodontists in the detection of periodontal bone loss on panoramic radiographs.Clinical oral investigations · 2026Article
- Deep Learning for the Assessment of Alveolar Bone Loss on Intraoral Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Accuracy of Deep Learning Models in Detecting Mandibular Furcation Defects on 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
- Accuracy of artificial intelligence applications in periodontics: a thematic narrative review.Frontiers in dental medicine · 2026Review
- Panoramic Radiograph-Based Deep Learning Models for Diagnosis and Clinical Decision Support of Furcation Lesions in Primary Molars.Children (Basel, Switzerland) · 2025Article
- Evaluation of Apical Closure in Panoramic Radiographs Using Vision Transformer Architectures ViT-Based Apical Closure Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Automatic Detection of Radiographic Alveolar Bone Loss in Bitewing and Periapical Intraoral Radiographs Using Deep Learning Technology: A Preliminary Evaluation.Diagnostics (Basel, Switzerland) · 2025Article
- ADGRU: Adaptive DenseNet with gated recurrent unit for automatic diagnosis of periodontal bone loss and stage periodontitis with tooth segmentation mechanism.Clinical oral investigations · 2024Article
- Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans.Journal of endodontics · 2024Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
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11 authors.
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Abstract
Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1-96.7%; AUC 0.944-0.970) and upper anterior (86.7-90.2%; 0.948-0.958) and lower (85.6-87.2%; 0.913-0.937) and upper posterior teeth (78.1-81.0%; 0.851-0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed.
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