ArticleBMC oral health2024
Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 3 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 3 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence-based deep learning models in detecting furcation involvement: A systematic review and meta-analysis.Journal of periodontology · 2026Pooled it
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 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
- Magnetic nanohydroxyapatite-peptide silk fibroin hydrogel induces osteogenesis in canine periodontal ligament stem cells revealed by proteomic analysis.The veterinary quarterly · 2026Article
- Deep Learning-Based Automated Diagnostic Charting on Panoramic Radiography: Comparison of YOLOv11 and YOLOv12.Odontology · 2026Article
- 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
- Reliability of the QUADAS-2 checklist in AI-based medical imaging: A comparison of human reviewers and ChatGPT-4o.Imaging science in dentistry · 2026Article
- Deep Learning for the Assessment of Alveolar Bone Loss on Intraoral Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Precision periodontology in clinical practice: bridging omics and clinical decision-making.Clinical oral investigations · 2026Review
- From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.Translational pediatrics · 2026Review
- Gonial Angle Estimation on Dental Panoramic X-Rays Using Deep Learning: A Transfer Learning Approach for Keypoint Detection.Journal of imaging informatics in medicine · 2026Article
- Evaluation of an AI-based segmentation model for mandibular fracture segmentation on CBCT using volumetric and 2D slice-based validation approaches.BMC oral health · 2026Article
- Deep learning-based identification, classification and segmentation of infraosseous periodontal defects using a YOLOv8 neural network.BMC oral health · 2026Article
- Opportunistic Assessment of Dental Pathologies in Cervical Computed Tomography Angiography: A Proof of Concept Study.Clinical neuroradiology · 2026Article
- 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
- AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns.Scientific reports · 2026Article
- Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on Bitewing Radiographs Using Deep Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Salivary microbial profiles across periodontal severity stage according to the 2018 AAP/EFP classifications: a cross-sectional study.Frontiers in oral health · 2026Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns.
methodsA total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis.
resultsThe system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively).
conclusionsAI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.
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