SynthesisBMC oral health2025
Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.
Synthesis in BMC oral health, 2025. 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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Who cites it
15 citing papers in PubMed.
- Deep Learning-Based Automated Diagnostic Charting on Panoramic Radiography: Comparison of YOLOv11 and YOLOv12.Odontology · 2026Article
- Review
- Tooth Detection and Numbering Performance of Two AI-Based Systems on Panoramic Radiographs: A Comparative Study.Dentistry journal · 2026Article
- Comparative Analysis of Third Molar Segmentation Performance Across Dental Developmental Stages and the 18-Year Age Threshold Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- Automated detection of periapical lesions in pediatric panoramic radiographs using YOLOv7: a retrospective internal validation study.BMC pediatrics · 2026Article
- Adversarial vulnerability and robustness of deep learning models for panoramic dental X-ray segmentation.Scientific reports · 2026Article
- Weighted knowledge distillation for semi-supervised segmentation of maxillary sinus in panoramic X-ray images.Scientific reports · 2026Article
- Real-Time Markerless Tooth Detection Towards Dynamic Robot-Assisted Dental Implant Navigation.Dentistry journal · 2026Article
- Artificial Intelligence in Dentistry: A Concise Review of Reporting Checklists and Guidelines.International dental journal · 2026Review
- A self attention based deep learning framework for accurate and efficient dental disease detection in OPG radiographs.Scientific reports · 2026Article
- Comparative Analysis of Deep Learning Architectures for Automatic Tooth Segmentation in Panoramic Dental Radiographs: Balancing Accuracy and Computational Efficiency.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning Analysis of CBCT Images for Periodontal Disease: Phenotype-Level Concordance with Independent Transcriptomic and Microbiome Datasets.Dentistry journal · 2025Article
- Automated Implant Placement Pathway from Dental Panoramic Radiographs Using Deep Learning for Preliminary Clinical Assistance.Diagnostics (Basel, Switzerland) · 2025Article
- U-Net-Based Deep Learning for Simultaneous Segmentation and Agenesis Detection of Primary and Permanent Teeth in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative evaluation of CNN architectures for detection of dental pulp calcifications on CBCT slices.Science progressArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis systematic review and meta-analysis aimed to summarize and evaluate the available information regarding the performance of deep learning methods for tooth detection and segmentation in orthopantomographies. MATERIAL AND
methodsElectronic databases (Medline, Embase and Cochrane) were searched up to September 2023 for relevant observational studies and both, randomized and controlled clinical trials. Two reviewers independently conducted the study selection, data extraction, and quality assessments. GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) assessment was adopted for collective grading of the overall body of evidence. From the 2,207 records identified, 20 studies were included in the analysis. Meta-analysis was conducted for the comparison of mesiodens detection and segmentation (n = 6) using sensitivity and specificity as the two main diagnostic parameters. A graphical summary of the analysis was also plotted and a Hierarchical Summary Receiver Operating Characteristic curve, prediction region, summary point, and confidence region were illustrated.
resultsThe included studies quantitative analysis showed pooled sensitivity, specificity, positive LR, negative LR, and diagnostic odds ratio of 0.92 (95% confidence interval [CI], 0.84-0.96), 0.94 (95% CI, 0.89-0.97), 15.7 (95% CI, 7.6-32.2), 0.08 (95% CI, 0.04-0.18), and 186 (95% CI, 44-793), respectively. A graphical summary of the meta-analysis was plotted based on sensitivity and specificity. Hierarchical Summary Receiver Operating Characteristic curves showed a positive correlation between logit-transformed sensitivity and specificity (r = 0.886).
conclusionsBased on the results of the meta-analysis and GRADE assessment, a moderate recommendation is advised to dental operators when relying on AI-based tools for tooth detection and segmentation in panoramic radiographs.
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Registered trials
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.