SynthesisLa Radiologia medica2023
Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.
Synthesis in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 2 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
- A comparative analysis of artificial intelligence generated 3d facial models from 2D photos: A procrustes-based validation of landmark accuracy in Orthodontics.Seminars in orthodontics · 2026Article
- Artificial Intelligence and Digital Technologies in Orthognathic and Reconstructive Maxillofacial Surgery: Data Availability and Evidence Maturity.Dentistry journal · 2026Review
- Immersive Virtual Reality for 3D Cephalometric Landmarking Across Low-Dose and Ultra-Low-Dose CBCT: A Pilot Comparison with a Conventional Computer Interface.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnostic accuracy of image-based deep learning for glaucomatous optic neuropathy detection: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- 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
- Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images.Diagnostics (Basel, Switzerland) · 2026Article
- Development and pilot validation of a three-dimensional skeletal coordinate-based system for occlusal vertical dimension assessment in elderly Korean cadavers.BMC oral health · 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
- CBCT-Based Orthodontic Classification Using Commercial AI: Completeness and Accuracy in Independent Validation.Journal of clinical medicine · 2026Article
- Lateral Cephalometric Radiography: Principles, Common Positioning Errors, and AI-Driven Quality Control.Diagnostics (Basel, Switzerland) · 2026Review
- 3D Correspondence Between Maxillary Central Incisor Morphology and Facial Shape in Young Adults.Dentistry journal · 2026Article
- Artificial Intelligence-Assisted Cephalometric Analysis: A Comparative Study Between WebCeph and NemoCeph.International journal of dentistry · 2026Article
- Automatic identification of anatomical landmarks in three-dimensional computed tomography/cone-beam computed tomography: a scoping review.Frontiers in dental medicine · 2026Review
- A personalized automated system of 3D facial soft tissue landmarks annotation based on deep learning and computer vision.BMC oral health · 2025Article
- Automatic Assessment of Radiological Parameters of the Distal Radius Using a Hybrid Approach Combining Deep Learning and a Computer-Aided Diagnostic Algorithm.Clinics in orthopedic surgery · 2025Article
- Attention-Base deep learning for 3D craniofacial soft tissue landmark detection and diagnosis in orthodontics.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectivesThe aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images.
methodsPubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication.
resultsThe initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I
conclusionDeep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done.
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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.