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Accuracy of cephalometric landmark identification on artificial intelligence-based software: a comparative study.

İpek Şavkan¹, Sara Nur Özçankaya¹, Öykü Naz Turan²

¹İstanbul Kent University, Faculty of Dentistry, Department of Orthodontics (Istanbul, Türkiye).

Dental Press Journal of Orthodontics

|May 19, 2026

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Artificial intelligence (AI) cephalometric software shows discrepancies in identifying specific anatomical landmarks compared to manual tracing. Clinicians should exercise caution with AI-only analyses for orthodontic diagnosis and treatment planning.

Area of Science:

Orthodontics
Radiographic analysis
Artificial Intelligence in Medicine

Background:

Cephalometry is crucial for orthodontic diagnosis and treatment planning.
Technological advancements have led to automated cephalometric analysis software.
This study compares manual and AI-based landmark identification accuracy.

Purpose of the Study:

To evaluate and compare the accuracy of manual vs. AI-based cephalometric landmark identification.
To assess discrepancies in landmark positioning between different analysis methods.
To inform clinical practice regarding AI tool utilization in orthodontics.

Main Methods:

25 lateral cephalometric radiographs analyzed.
Manual landmark identification by two orthodontists using NemoCeph.

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Comparison with AI landmark identification from CephNinja and WebCeph™.

Evaluation of 17 common hard and soft tissue landmarks.

Statistical analysis using ANOVA and ICC (p < 0.05, p < 0.01).

Main Results:

Statistically significant differences found for porion, basion, gonion, and incisor apexes (p < 0.05).
AI software generally positioned landmarks more distally and superiorly than manual tracings.
CephNinja showed higher similarity to manual tracings for gonion and basion.
WebCeph™ demonstrated closer results to manual tracings for incisor apexes.

Conclusions:

AI cephalometric software provides rapid, reproducible results but has limitations.
Significant positional discrepancies exist for key landmarks (porion, basion, gonion, incisor apices).
Clinicians must be cautious using solely automated AI analyses for diagnosis and treatment planning due to algorithmic limitations.