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Evaluation of orthognathic surgery planning with artificial intelligence: a prospective, comparative study
Enes Temizkan1, B Zeynep Yörük2, Banu Kılıç1
1Department of Orthodontics, Faculty of Dentistry, Bezmialem Vakıf University, Istanbul Türkiye.
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.
