SynthesisInternational dental journal2024
Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.
Synthesis in International dental journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Development of an automated YOLO v8-based deep learning teeth numbering model for digital orthopantomogram.Oral radiology · 2026Article
- Review
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- Automated Tooth Detection and Caries Identification in CBCT With Deep Learning.International dental journal · 2026Article
- Deep learning-based assessment of periapical radiographic image quality.Scientific reports · 2026Article
- Artificial Intelligence-Aided Tooth Detection and Segmentation on Pediatric Panoramic Radiographs in Mixed Dentition Using a Transfer Learning Approach.Diagnostics (Basel, Switzerland) · 2025Article
- [An lightweight algorithm for multi-dimensional optimization of intelligent detection of dental abnormalities on panoramic oral X-ray images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion, Crown, Restoration, Dental Caries) in Intraoral Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.International dental journal · 2025Article
- Artificial Intelligence for Tooth Detection in Cleft Lip and Palate Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Challenges in Pedicatric Orthodontics and Radiology: Evidence from Recent Studies and Implications for Clinical Practice: A Narrative Review.Galen medical journal · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was undertaken to critically appraise developments and performance of artificial intelligence (AI) models designed for tooth numbering and detection using dento-maxillofacial radiographic images. In order to maintain the integrity of their methodology, the authors of this systematic review followed the diagnostic test accuracy criteria outlined in PRISMA-DTA. Electronic search was done by navigating through various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library for the articles published from 2018 to 2023. Sixteen articles that met the inclusion exclusion criteria were subjected to risk of bias assessment using QUADAS-2 and certainty of evidence was assessed using GRADE approach.AI technology has been mainly applied for automated tooth detection and numbering, to detect teeth in CBCT images, to identify dental treatment patterns and approaches. The AI models utilised in the studies included exhibited a highest precision of 99.4% for tooth detection and 98% for tooth numbering. The use of AI as a supplementary diagnostic tool in the field of dental radiology holds great potential.
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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.