SynthesisBMC oral health2025
Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.
Synthesis in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
21 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
- Efficacy of Automatic 3D Segmentation of the Upper Airway in CBCT or CT Scans via Artificial Intelligence Versus Manual Segmentation by Human Experts: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- A novel fluoride tray-assisted workflow for artificial intelligence-driven automated maxillary gingival segmentation on cone beam CT.Dento maxillo facial radiology · 2026Article
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- Validation of artificial intelligence-assisted CBCT analysis for predicting inferior alveolar nerve proximity to impacted mandibular third molars: a diagnostic accuracy study.BMC oral health · 2026Article
- Letter to the Editor on Generative AI-Enhanced Low-Dose CBCT for Impacted Mandibular Third Molars.International dental journal · 2026Article
- Letter to the Editor on AI-Based Segmentation of Cemento-Osseous Dysplasias.International dental journal · 2026Article
- Review
- AI-Assisted Identification of the Medial Lingual Foramen on CBCT: A Deep Learning Approach for Preoperative Implant Assessment.Medicina (Kaunas, Lithuania) · 2026Article
- The Use of Artificial Intelligence in Planning Dental Implant Procedures: A Systematic Review.Dentistry journal · 2026Review
- CBCT Image Superimposition for Longitudinal Monitoring of Mandibular Cyst Healing: A Technical Note.Clinical and experimental dental research · 2026Article
- Artificial Intelligence-enabled Automatic Computed Tomography Segmentation for Craniomaxillofacial Surgery Using a Real Surgical Database.Plastic and reconstructive surgery. Global open · 2026Article
- Accuracy, precision, and efficiency of manual planimetry versus point counting for sectional area estimation in oral morphometry: a comparison using a simulated two-dimensional reference model.Frontiers in oral health · 2026Article
- A deep learning based automated maxillary sinus segmentation and bone grafts analysis in CBCT images.NPJ digital medicine · 2025Article
- Trueness of artificial intelligence-based, manual, and global thresholding segmentation protocols for human mandibles.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
- Fully Automated AI-Based Digital Workflow for Mirroring of Healthy and Defective Craniofacial Models.Journal of imaging · 2025Article
- Accuracy comparison between active robot and freehand techniques in modified mid-segment-first extraction of impacted mandibular third molars: an in vitro study.Journal of robotic surgery · 2025Article
- Development and verification of a convolutional neural network-based model for automatic mandibular canal localization on multicenter CBCT images.BMC oral health · 2025Article
- Correction to: Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.BMC oral health · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans. MATERIALS AND
methodsArticles were retrieved from MEDLINE, Cochrane CENTRAL, IEEE Xplore, and Google Scholar. Eligible studies were analyzed thematically, and their quality was appraised using the JBI checklist for diagnostic test accuracy studies. Meta-analysis was conducted for key performance metrics, including Dice Similarity Coefficient (DSC) and Average Surface Distance (ASD).
resultsA total of 767 non-duplicate articles were identified, and 30 studies were included in the review. Of these, 27 employed deep-learning models, while 3 utilized classical machine-learning approaches. The pooled DSC for mandible segmentation was 0.94 (95% CI: 0.91-0.98), mandibular canal segmentation was 0.694 (95% CI: 0.551-0.838), maxilla segmentation was 0.907 (95% CI: 0.867-0.948), and teeth segmentation was 0.925 (95% CI: 0.891-0.959). Pooled ASD values were 0.534 mm (95% CI: 0.366-0.703) for the mandibular canal, 0.468 mm (95% CI: 0.295-0.641) for the maxilla, and 0.189 mm (95% CI: 0.043-0.335) for teeth. Other metrics, such as sensitivity and precision, were variably reported, with sensitivity exceeding 90% across studies.
conclusionAI-based segmentation, particularly using deep-learning models, demonstrates high accuracy in the segmentation of dental and maxillofacial structures, comparable to expert manual segmentation. The integration of AI into clinical workflows offers not only accuracy but also substantial time savings, positioning it as a promising tool for automated dental imaging.
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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.