ArticleEuropean oral research2026
Evaluation of orthognathic surgery planning with artificial intelligence: a prospective, comparative study.
Article in European oral research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to evaluate the accuracy of cephalometric analyses performed by deep learning-based AI programs (NemoCeph 2D, OrthoDx, AudaxCeph, and WebCeph) by comparing their results with the gold standard measurements obtained from 3D CT scans in orthognathic surgery patients. Materials and Methods: Orthognathic surgery candidates underwent pretreatment 3D CT scans. These scans were manually analyzed using 3D cephalometric software to establish gold-standard landmark positions. Two-dimensional cephalometric images were then derived from the 3D scans, and deep learning-based AI programs automatically identified the landmarks on these images. The AI-generated measurements were compared with the 3D gold standard, and the differences were analyzed statistically. Results: While the ANB angle showed no significant differences between the methods (p=0.061), other measurements-including SNA, SNB, Wits appraisal, Y Axis Angle, and various facial height ratios-showed significant discrepancies (p<0.05). Conclusion: AI-based cephalometric analyses showed notable errors compared with the 3D CT gold standard. These findings suggest that deep learning algorithms require further refinement before they can be reliably used for orthognathic surgery planning.
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