ReviewDento maxillo facial radiology2022
Application of deep learning in teeth identification tasks on panoramic radiographs.
Review in Dento maxillo facial radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.Dento maxillo facial radiology · 2024Pooled it
- Development and Validation of AI System for Tooth Detection and Diagnosis in Dental Radiographs.International dental journal · 2026Article
- Image Resolution's Impact on Artificial Intelligence & Human Accuracy in Full-Mouth Radiographic Analysis.International dental journal · 2026Article
- Assessment of artificial intelligence in detecting errors on panoramic radiographs.Imaging science in dentistry · 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 in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O'Leary Index.Diagnostics (Basel, Switzerland) · 2025Article
- Orthopantomogram teeth segmentation and numbering dataset.Data in brief · 2024Article
- Evaluation of Dental Panoramic Radiographs by Artificial Intelligence Compared to Human Reference: A Diagnostic Accuracy Study.Journal of clinical medicine · 2024Article
- Artificial Intelligence (AI) Assessment of Pediatric Dental Panoramic Radiographs (DPRs): A Clinical Study.Pediatric reports · 2024Article
- Combining public datasets for automated tooth assessment in panoramic radiographs.BMC oral health · 2024Article
- Artificial intelligence-based automated preprocessing and classification of impacted maxillary canines in panoramic radiographs.Dento maxillo facial radiology · 2024Article
- Deep learning and artificial intelligence in dental diagnostic imaging.The Japanese dental science review · 2023Review
- Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews.Dento maxillo facial radiology · 2023Review
- Application of Cone Beam Computed Tomography in Risk Assessment of Lower Third Molar Surgery.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning for tooth identification and enumeration in panoramic radiographs.Dental research journal · 2023Article
- Convolutional neural network-based automatic cervical vertebral maturation classification method.Dento maxillo facial radiology · 2022Article
- Deep learning for automated detection and numbering of permanent teeth on panoramic images.Dento maxillo facial radiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the current developments of Artificial Intelligence (AI) in teeth identification on Panoramic Radiographs (PR). Our aim was to evaluate and compare the performances of Deep Learning (DL) models that have been employed in the execution of this task.
methodsThe systematic review was registered on PROSPERO. All recent studies that utilized DL models for identifying teeth on PRs were included in this review. An extensive search of the medical electronic databases including PubMed NLM, EBSCO Dentistry & Oral Sciences Source, and Wiley Cochrane Library was conducted. This was followed by a hand search of the IEEE Xplore database. The diagnostic performance of DL models in teeth identification tasks on PR was the primary outcome assessed in this review. The risk of bias assessment of the included studies was evaluated via the modified QUADAS-2 tool. Owing to the heterogeneity of the reported performance metrics, a meta-analysis was not possible..
resultsThe search yielded a total of 282 articles, out of which 13 relevant ones were included in this review. These studies utilized a diverse range of DL models for teeth identification tasks on PRs and reported their performances using a variety of metrics.
conclusionThe results of teeth identification tasks carried out by DL models are encouraging; however, there is a need for the shortcomings that have been identified in our preliminary review, to be addressed by future researchers.
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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.