ReviewJournal of clinical and experimental dentistry2026
Evaluation of Accuracy and Reproducibility of an AI-Based Cephalometric Landmark Detection Software: A Comparative Study with Manual Annotations.
Review in Journal of clinical and experimental dentistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
Background: Automated cephalometric landmark detection using artificial intelligence (AI) has emerged as a promising aid in orthodontic diagnosis and treatment planning. However, its clinical usefulness depends on both accuracy of detection and quality of the reference annotations that are used for evaluation. Objective: To assess the accuracy, precision and stability of an AI-based cephalometric landmark detection system (WebcephTm) using a rigorously validated ground truth. Material and Methods: One hundred lateral cephalograms were independently annotated by expert orthodontists. A custom-developed application was used to extract and standardize landmark coordinates in millimeters from radiographic images. Inter-observer agreement validated the ground truth. AI-annotated landmarks were compared with expert annotations using Euclidean distance measurements. Landmark-dependent variability was examined, and the stability of AI behavior across images was evaluated using the coefficient of variation (CV). Results: Inter-observer analysis demonstrated excellent agreement (ICC=1; mean discrepancy 1,53+- 0.70 mm), confirming the reliability of the reference. The AI system showed a mean localization error of 3,16 mm, with only 42,7% of detections within the 2-mm clinical threshold. Performance was strongly landmark-dependent. Soft-tissue landmarks showed higher localization errors than skeletal landmarks, although this difference was not statistically significant (Mann-Witney U test, p > 0,05). CV analysis confirmed that AI errors were anatomically driven rather than randomly distributed. Conclusions: AI-based cephalometric detection by WebcephTm is reliable for well-defined skeletal landmarks, while soft-tissue points remain challenging. The observed limitations appear to be primarily related to anatomical and radiographic complexity rather than to reference annotation inaccuracy or algorithmic instability.
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Registered trials
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