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Reproducibility of AI in Cephalometric Landmark Detection: A Preliminary Study.

David Emilio Fracchia1, Denis Bignotti1, Stefano Lai1

  • 1Department of Surgical Sciences, Postgraduate School in Orthodontics, University of Cagliari, 09124 Cagliari, Italy.

Diagnostics (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

Artificial intelligence (AI) shows superior reproducibility in identifying cephalometric landmarks compared to manual tracing. AI offers a reliable tool for digital cephalometry, though some landmarks remain challenging.

Keywords:
anatomic landmarksartificial intelligencecephalometrycomputer-assistedcorrectiveimage processingorthodontics

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cephalometric landmark identification is crucial in orthodontics.
  • Manual tracing can be subjective and time-consuming.
  • AI offers potential for automated and reproducible cephalometric analysis.

Purpose of the Study:

  • To compare the reproducibility of AI-driven cephalometric landmark identification with manual tracing.
  • To evaluate AI performance under different image modification conditions.

Main Methods:

  • A single lateral cephalogram was analyzed using AI (WebCeph) and manual tracing.
  • Eighteen cephalometric landmarks were traced 10 times by AI (with/without image modification) and an orthodontist.
  • Reproducibility was assessed using coefficient of variation and centroid distance analysis.

Main Results:

  • AI demonstrated higher reproducibility than manual tracing, with lower mean CV values.
  • AI methods were more consistent, especially for Menton and Pogonion landmarks.
  • Posterior Nasal Spine showed significant improvement with AI (p=0.001), while Gonion remained variable.

Conclusions:

  • AI offers superior reproducibility for cephalometric landmark identification compared to manual methods.
  • AI is a promising adjunct for digital cephalometry, but challenges remain for specific landmarks.
  • Further multi-image studies are needed to validate clinical applicability.