ReviewJournal of clinical and experimental dentistry2023
Performance of artificial intelligence using cone-beam computed tomography for segmentation of oral and maxillofacial structures: A systematic review and meta-analysis.
Review in Journal of clinical and experimental dentistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.Oral radiology · 2026Pooled it
- Capabilities of Cephalometric Methods to Study X-rays in Three-Dimensional Space (Review).Sovremennye tekhnologii v meditsine · 2024Pooled it
- Review
- Artificial Intelligence and Digital Workflow in Craniofacial Bone Tissue Engineering: From Cone-Beam Computed Tomography (CBCT) to Personalized Bioceramic Implants.Dentistry journal · 2026Review
- Anatomical characteristics of mental foramen and canal: A cone-beam computed tomography analysis.Journal of clinical and experimental dentistry · 2024Article
- Augmented Reality-Guided Extraction of Fully Impacted Lower Third Molars Based on Maxillofacial CBCT Scans.Bioengineering (Basel, Switzerland) · 2024Article
- A Comparative Analysis of Artificial Intelligence and Manual Methods for Three-Dimensional Anatomical Landmark Identification in Dentofacial Treatment Planning.Bioengineering (Basel, Switzerland) · 2024Article
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
Background: There are different values reported about the performance of artificial intelligence using cone-beam computed tomography (CBCT) for segmentation of oral and maxillofacial structures. We aimed to perform a systematic review and meta-analysis to provide an overall estimate to resolve the given conflicts. Material and Methods: A literature search was conducted in Embase, PubMed, and Scopus through 31 October 2022, to identify studies evaluating artificial intelligence systems using oral and maxillofacial CBCT images for automatic segmentation of anatomical landmarks. The surveys had to report the outcome according to dice coefficient (DICE) or dice similarity coefficient (DSC) index. The estimates were presented as percent and 95% confidence interval (CI). I-squared index was used to assess the heterogeneity between the surveys. Results: A total of 24 eligible studies were finally enrolled. The overall pooled DICE/DSC value for artificial intelligence was 0.92 (95% CI: 0.88-0.95; I-squared=93.6%, Conclusions: The findings revealed excellent performance for the artificial intelligence regarding the segmentation task of oral and maxillofacial CBCT images.
Identifiers
What OpenQuestion holds
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.