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Beyond Geometric Deformation: High-Fidelity Orthodontic Profile Synthesis via ControlNet-Guided Generative AI.
Fengcong Wang1, Yang Lyu2, Yilan Yang3
1Department of Orthodontics, Jinan Stomatological Hospital, Shandong Provincial Health Commission Key Laboratory of Oral Diseases and Tissue Regeneration, Jinan, Shandong Province, 250000, China..
Journal of Dentistry
|July 15, 2026
Summary
This study introduces a novel AI framework for creating realistic orthodontic profile visualizations. The technology accurately predicts lower facial outcomes, aiding patient communication and expectation management in orthodontics.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Orthodontics
Background:
- Orthodontic treatment planning often relies on two-dimensional cephalometric analysis.
- Visualizing potential post-treatment facial outcomes is crucial for patient communication and managing expectations.
- Current visualization methods may lack photorealism or precise contour adherence.
Purpose of the Study:
- To develop and validate a diffusion-based AI framework for synthesizing photorealistic orthodontic profile visualizations.
- To ensure the synthesized profiles accurately reflect lower-facial outcomes guided by cephalometric contours.
- To assess the clinical applicability and perceptual realism of the generated visualizations.
Main Methods:
- A retrospective dataset of cephalograms and profile photographs from ten adult Asian female patients was utilized.
- A Stable Diffusion inpainting model, conditioned by ControlNet, synthesized lower facial profiles.
- Quantitative evaluation included landmark localization error and image quality metrics (LPIPS, SSIM, PSNR).
- Subjective evaluation involved a Visual Turing Test (VTT) and realism ratings by orthodontists and laypeople.
Main Results:
- The framework achieved a mean landmark error of 1.38 ± 0.13 mm, with 95% within the 2.0 mm clinical threshold.
- High image fidelity was confirmed by LPIPS, SSIM, and PSNR metrics.
- Orthodontists detected generated images with 71.0% accuracy in the VTT, while laypeople performed at chance level (51.3%).
- Realism ratings showed no significant difference between generated and real images.
Conclusions:
- The ControlNet-guided diffusion framework successfully synthesizes orthodontic visualizations with high adherence to cephalometric contours and perceptual realism.
- This AI-driven approach can serve as a valuable tool for patient communication and expectation management in orthodontics.
- The synthesized realistic profile visualizations enhance the understanding of potential treatment outcomes.
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