ReviewPolish journal of radiology2023
Accuracy of artificial intelligence in the detection and segmentation of oral and maxillofacial structures using cone-beam computed tomography images: a systematic review and meta-analysis.
Review in Polish journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis 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.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Artificial Intelligence and Digital Workflow in Craniofacial Bone Tissue Engineering: From Cone-Beam Computed Tomography (CBCT) to Personalized Bioceramic Implants.Dentistry journal · 2026Review
- Automated tooth numbering on panoramic radiographs versus cone-beam computed tomographs: a diagnostic accuracy study of a commercial artificial intelligence system.Dento maxillo facial radiology · 2026Article
- Radiographic assessment of post-endodontic filling features on PAN and CBCT: diagnostic agreement of an AI platform against CBCT consensus.Scientific reports · 2026Article
- Artificial Intelligence-enabled Automatic Computed Tomography Segmentation for Craniomaxillofacial Surgery Using a Real Surgical Database.Plastic and reconstructive surgery. Global open · 2026Article
- Biomaterial strategies for precision regeneration of the tooth extraction socket: integrating site phenotype, pathological microenvironment, and healing sequence.Frontiers in bioengineering and biotechnology · 2026Review
- Detection accuracy of an AI platform for dental treatment features on panoramic radiographs - tooth- and patient-level analyses.Scientific reports · 2025Article
- AI-Based Detection of Dental Features on CBCT: Dual-Layer Reliability Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- Assessing the accuracy of artificial intelligence in mandibular canal segmentation compared to semi-automatic segmentation on cone-beam computed tomography images.Polish journal of radiology · 2025Article
- Evaluation of a Vendor-Agnostic Deep Learning Model for Noise Reduction and Image Quality Improvement in Dental CBCT.Diagnostics (Basel, Switzerland) · 2024Article
- Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.Journal of clinical medicine · 2024Review
- Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics.Journal of clinical medicine · 2024Article
- Reliability of the AI-Assisted Assessment of the Proximity of the Root Apices to Mandibular Canal.Journal of clinical medicine · 2024Article
- The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Evaluating the Diagnostic Accuracy of an AI-Driven Platform for Assessing Endodontic Treatment Outcomes Using Panoramic Radiographs: A Preliminary Study.Journal of clinical medicine · 2024Article
- Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy.Journal of clinical medicine · 2024Article
- A Comparative Analysis of Artificial Intelligence and Manual Methods for Three-Dimensional Anatomical Landmark Identification in Dentofacial Treatment Planning.Bioengineering (Basel, Switzerland) · 2024Article
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.Journal of clinical medicine · 2024Review
- Performance of different cone-beam computed tomography scan modes with and without metal artifact reduction in detection of recurrent dental caries under various restorative materials.Polish journal of radiology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The aim of the present systematic review and meta-analysis was to resolve the conflicts on the diagnostic accuracy of artificial intelligence systems in detecting and segmenting oral and maxillofacial structures using cone-beam computed tomography (CBCT) images. Material and methods: We performed a literature search of the Embase, PubMed, and Scopus databases for reports published from their inception to 31 October 2022. We included studies that explored the accuracy of artificial intelligence in the automatic detection or segmentation of oral and maxillofacial anatomical landmarks or lesions using CBCT images. The extracted data were pooled, and the estimates were presented with 95% confidence intervals (CIs). Results: In total, 19 eligible studies were identified. As per the analysis, the overall pooled diagnostic accuracy of artificial intelligence was 0.93 (95% CI: 0.91-0.94). This rate was 0.93 (95% CI: 0.89-0.96) for anatomical landmarks based on 7 studies and 0.92 (95% CI: 0.90-0.94) for lesions according to 12 reports. Moreover, the pooled accuracy of detection and segmentation tasks for artificial intelligence was 0.93 (95% CI: 0.91-0.94) and 0.92 (95% CI: 0.85-0.95) based on 14 and 5 surveys, respectively. Conclusions: Excellent accuracy was observed for the detection and segmentation objectives of artificial intelligence using oral and maxillofacial CBCT images. These systems have the potential to streamline oral and dental healthcare services.
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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.