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Clinical Reliability of AI-Based Cephalometric Analysis Using WebCeph: A Comparative Agreement Study.

Ali Azari-Mehr¹, Angela Bisbal-Puchades¹, Laura Marqués-Martínez¹

¹Dentistry Department, Medicine and Health Science Faculty, Catholic University of Valencia, 46001 Valencia, Spain.

Journal of Clinical Medicine

|June 12, 2026

Summary

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AI cephalometric analysis shows significant differences compared to expert manual tracing. Automated tools like WebCeph may not be clinically interchangeable for key diagnostic variables, suggesting adjunctive use.

Area of Science:

Dentistry
Artificial Intelligence
Medical Imaging Analysis

Background:

Artificial intelligence (AI) offers rapid, standardized cephalometric analysis.
Clinical interchangeability of AI measurements with expert manual tracing is uncertain.

Purpose of the Study:

To compare automated AI cephalometric analysis (WebCeph) with expert manual tracing.
To evaluate the clinical agreement of AI-generated Steiner cephalometric variables.

Main Methods:

Comparative study of 100 lateral cephalometric radiographs.
Analysis using expert manual tracing and automated WebCeph platform.
Evaluation of seven Steiner variables using paired t-tests, ICC, and Bland-Altman analysis.

Main Results:

Keywords:

artificial intelligence cephalometry diagnostic imaging orthodontics reproducibility of results

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Six of seven variables showed statistically significant differences between AI and manual methods.
Automated measurements tended to overestimate skeletal and dental parameters.
Intraclass correlation coefficients (ICC) were poor for clinically relevant variables (e.g., ANB, FMA), indicating low agreement.

Conclusions:

WebCeph AI analysis lacks clinically acceptable agreement with expert manual tracing for key cephalometric variables.
Discrepancies in sagittal and vertical diagnostic parameters may impact clinical interpretation and treatment planning.
AI cephalometric analysis is best utilized as an adjunctive tool, not a replacement for clinician evaluation.