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Related Experiment Video

Updated: Jun 23, 2025

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and

Julie Hendrickx¹, Rellyca Sola Gracea^2,3,4, Michiel Vanheers¹

¹Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.

European Journal of Orthodontics

|June 19, 2024

Summary

Artificial intelligence (AI) shows promise for accurate and efficient cephalometric landmark detection on 2D cephalograms and 3D CBCT images. While 2D analysis met accuracy thresholds, 3D analysis requires further research due to heterogeneity.

Keywords:

anatomic landmarks artificial intelligence cephalometry orthodontics

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Last Updated: Jun 23, 2025

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Published on: September 8, 2023

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Area of Science:

Radiology and Imaging
Artificial Intelligence in Medicine
Dental Diagnostics

Background:

Cephalometric analysis is crucial for diagnosing and planning orthodontic and surgical treatments.
Traditional manual landmark identification is time-consuming and prone to inter-observer variability.
Automated landmark detection using artificial intelligence (AI) offers a potential solution to improve efficiency and accuracy.

Purpose of the Study:

To systematically review and meta-analyze the accuracy and efficiency of AI-driven automated landmark detection.
To evaluate AI performance on both 2D lateral cephalograms and 3D cone-beam computed tomographic (CBCT) images.
To identify the current state and limitations of AI in cephalometric analysis.

Main Methods:

A comprehensive electronic search was conducted across PubMed, Web of Science, Embase, and grey literature up to January 2024.
Studies utilizing AI for 2D or 3D cephalometric landmark detection were included.
Meta-analysis was performed for 2D landmark accuracy (mean radial error), while 3D accuracy was qualitatively synthesized due to heterogeneity. Risk of bias was assessed.

Main Results:

34 publications were selected, with 27 focusing on 2D cephalograms and 7 on 3D CBCT images.
Meta-analysis of 2D images showed a mean error of 1.39 mm, below the 2 mm clinical threshold.
3D landmark detection error ranged from 1.0 to 5.8 mm; meta-analysis was not feasible. Both 2D and 3D AI methods were time-efficient (<1 min).
A high risk of bias was noted in data selection and reference standards for most studies.

Conclusions:

AI-driven cephalometric landmark detection demonstrates potential for accuracy and time efficiency in both 2D and 3D imaging.
Further improvements in generalizability and robustness of AI systems are needed.
AI holds promise for revolutionizing cephalometric analysis, but methodological rigor and validation are essential.