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Deep-learning-based assessment of postoperative nasolabial morphology in unilateral cleft lip repair
Petr Šimánek1, Christiane Keil2, Tom Alexander Schröder3
1Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic.
Background:
Objective assessment of postoperative facial appearance following unilateral cleft lip and palate (UCLP) repair remains limited by subjective evaluation and operator-dependent morphometric methods. This study developed and clinically validated a fully automated deep-learning-based framework for objective assessment of postoperative nasolabial morphology.
Methods:
Standardized frontal photographs of postoperative UCLP patients (n = 39) and healthy controls (n = 57) were analyzed. Anatomically defined regions of interest of the nose, upper lip, and combined nasolabial complex were extracted using the MediaPipe 468-point facial landmark model. Diffusion-based generative inpainting was applied to generate individualized healthy reference appearances. Original and generated images were compared using semantic, perceptual, and geometric metrics, including DINOv2 feature distances, LPIPS, SSIM, and landmark-derived symmetry parameters.
Results:
Postoperative UCLP patients demonstrated significantly greater deviation from healthy facial appearance patterns compared with controls across multiple metrics. DINOv2 feature distances consistently differentiated postoperative patients from controls, indicating persistent semantic deviations in nasolabial morphology following cleft repair. Landmark-based geometric analysis additionally demonstrated significantly increased residual lip and nasal asymmetry in the postoperative cohort. The differential behavior of the generative model between healthy and postoperative faces further suggested that the magnitude of generative correction reflects residual postoperative deformity.
Conclusion:
The proposed framework enables automated and observer-independent assessment of postoperative nasolabial morphology following UCLP repair. By integrating anatomy-informed landmark analysis with AI-driven morphometric evaluation, the method provides a scalable tool for retrospective outcome analysis and standardized comparison of surgical results in craniofacial surgery.
