Interlayer-aware postoperative facial appearance prediction in orthognathic surgery with bio-geometric guidance

OBJECTIVE: Accurate prediction of postoperative facial appearance is essential for orthognathic surgical planning, yet remains challenging due to the nonlinear biomechanical coupling between bone and soft tissue. While deep learning methods offer a faster alternative to traditional biomechanical simulation, they typically map bony displacements directly to facial surface deformation, overlooking the intervening soft-tissue layers with distinct biomechanical properties through which displacement is progressively transmitted. Moreover, the many-to-one effect of regional bony movements on each soft-tissue point remains insufficiently captured. APPROACH: We propose a novel network that introduces an Interlayer Mixture-of-Experts mechanism to decouple bone-to-surface deformation propagation into three biomechanically inspired proxy representations at different tissue depth levels. Since the contribution of each depth level varies across individuals and facial regions, a gated routing network adaptively weights each layer's contribution, providing a data-driven approximation of spatially heterogeneous, patient-specific deformation transmission. Additionally, a Bio-Geometric Convolution module captures regional bony influences through elastic-weighted neighborhood aggregation. MAIN RESULTS: Evaluations on 88 orthognathic surgery patients demonstrate that the proposed method achieves a mean whole-face error of 1.39 mm, below the 2 mm clinical threshold, with statistically significant improvements over all baselines (p < 0.05) and the highest clinician acceptance rate (94%). SIGNIFICANCE: By incorporating bio-geometric priors into a deep learning framework, our approach enables a more physically grounded and interpretable prediction paradigm, supporting efficient and clinically reliable surgical planning.