Current Evidence on Artificial Intelligence for Cephalometric Diagnosis and Orthodontic Treatment Planning - A Narrative Review
DOI:
https://doi.org/10.12775/JEHS.2026.92.72449Keywords
artificial intelligence, deep learning, convolutional neural networks, cephalometric analysis, landmark identification, orthodontics, CBCT, treatment planning, growth assessmentAbstract
Background
Cephalometric analysis is a fundamental diagnostic tool in orthodontics, enabling clinicians to assess craniofacial morphology, classify skeletal relationships, and develop treatment plans. The use of artificial intelligence (AI) has created new opportunities to automate and standardize this diagnostic process, making everyday practice more efficient and less prone to error.
Aim
The aim of this study was to examine the application of artificial intelligence algorithms in cephalometric landmark identification and orthodontic treatment planning, with particular focus on their accuracy, comparison with human expert performance, clinical limitations, and future perspectives.
Materials and Methods
A narrative review of the current literature was conducted, focusing on the use of artificial intelligence mechanisms in image analysis, their accuracy, and future potential for full autonomy.
Results
The available literature indicates that AI-based systems consistently achieve clinically acceptable accuracy in automated cephalometric landmark detection on 2D lateral cephalograms. The proportion of landmarks detected within the 2 mm clinical threshold frequently matches experienced clinician performance. Three-dimensional CBCT-based landmarking demonstrated improving but still inferior accuracy compared to 2D analysis.
Conclusions
Artificial intelligence has become a reliable supporting tool in 2D cephalometric landmark identification, with current systems achieving the accuracy of experienced orthodontists within the clinical threshold. Three-dimensional CBCT analysis and AI-assisted treatment planning are advancing rapidly but are not yet equally reliable. Insufficient ethnic diversity in training datasets and domain shift across imaging devices remain ongoing concerns. AI should be regarded as an instrument that supports, not replaces, the expertise and responsibility of the orthodontist.
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