SynthesisJournal of digital imaging2023
Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis.
Synthesis in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 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.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Parameter-level comparison of artificial intelligence and manual cephalometric measurements: a systematic review and meta-analysis.Frontiers in dental medicine · 2026Pooled it
- Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.European journal of orthodontics · 2024Pooled it
- Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.La Radiologia medica · 2023Pooled it
- Artificial Intelligence and Digital Technologies in Orthognathic and Reconstructive Maxillofacial Surgery: Data Availability and Evidence Maturity.Dentistry journal · 2026Review
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Left-Right Consistency and Quantification of Craniofacial Asymmetry by Two Fully Automatic AI-Powered Three-Dimensional Cephalometry Systems on CBCT.Diagnostics (Basel, Switzerland) · 2026Article
- Agreement of WebCeph-Based Automated and Expert-Adjusted Cephalometric Analyses with Manual and Dolphin Tracings.Diagnostics (Basel, Switzerland) · 2026Article
- Reliability of Automated Cephalometric Analysis: A Comparative Assessment of Stratification Strategies Based on Chronological Age Versus Dentition Stage.Dentistry journal · 2026Article
- Reliability of AI-driven cephalometric analysis on CBCT: comparison with manual 2D cephalometry.Progress in orthodontics · 2026Article
- Clinical accuracy of cephalometric analysis using deep learning-based automated landmark identification on CBCT in class I and class II malocclusions.Scientific reports · 2026Article
- CBCT-Based Orthodontic Classification Using Commercial AI: Completeness and Accuracy in Independent Validation.Journal of clinical medicine · 2026Article
- Oral radiologists and orthodontists' attitude regarding the use of artificial intelligence for cephalometric analysis.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2026Article
- Automatic identification of anatomical landmarks in three-dimensional computed tomography/cone-beam computed tomography: a scoping review.Frontiers in dental medicine · 2026Review
- Evaluation of an Automatic Cephalometric Superimposition Method Based on Feature Matching.Journal of imaging informatics in medicine · 2025Article
- Comparison of 2D, 2.5D, and 3D landmark localization networks for 3D cephalometry in CT images.BMC oral health · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Accuracy of artificial intelligence-assisted soft tissue landmark identification in serial lateral cephalograms of Class III two-jaw surgery patients.Korean journal of orthodontics · 2025Article
- Influence of head positioning errors on the accuracy of fully automated artificial intelligence-based cephalometric software.The Angle orthodontist · 2025Article
- Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.BMC oral health · 2025Article
- Automatic detection of developmental stages of molar teeth with deep learning.BMC oral health · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76-82%, I
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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.