Beyond Geometric Deformation: High-Fidelity Orthodontic Profile Synthesis via ControlNet-Guided Generative AI.

OBJECTIVE: To develop and validate a clinically oriented, diffusion-based framework for orthodontic profile visualization that synthesizes photorealistic lower-facial outcomes under explicit cephalometric contour guidance. METHODS: Retrospective paired post-treatment cephalograms and profile photographs of ten adult Asian female patients were collected. Soft-tissue profiles were traced and rigidly registered to photographs. The lower facial third was synthesized using a Stable Diffusion inpainting model conditioned by ControlNet. Performance was evaluated quantitatively via landmark localization error and image quality metrics, including Learned Perceptual Image Patch Similarity (LPIPS), Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). Subjective evaluation included a Visual Turing Test (VTT) and realism ratings by orthodontists and laypeople. RESULTS: The mean landmark error was 1.38 ± 0.13 mm, with 95.0% of deviations within the 2.0 mm clinical threshold. Image quality metrics demonstrated high fidelity (LPIPS: 0.089 ± 0.024; SSIM: 0.951 ± 0.009, PSNR: 28.69 ± 2.47 dB). In the VTT, laypeople performed at chance level (accuracy: 51.3% ± 13.0%; P > 0.05), whereas orthodontists showed higher detection accuracy (71.0% ± 9.9%; P < 0.001). Realism ratings did not differ between generated and real images using the Wilcoxon signed-rank test (P = 0.579). CONCLUSION: The proposed ControlNet-guided diffusion framework effectively synthesizes orthodontic visualizations that show favorable adherence to post-treatment cephalometric contours and high perceptual realism. CLINICAL SIGNIFICANCE: This profile synthesis approach may serve as an adjunct for patient communication and expectation management by converting user-defined contours into realistic profile visualizations.