A novel RBF-based predictive tool for facial distraction surgery in growing children with syndromic craniosynostosis

PURPOSE: Predicting changes in face shape from corrective surgery is challenging in growing children with syndromic craniosynostosis. A prediction tool mimicking composite bone and skin movement during facial distraction would be useful for surgical audit and planning. To model surgery, we used a radial basis function (RBF) that is smooth and continuous throughout space whilst corresponding to measured distraction at landmarks. Our aim is to showcase the pipeline for a novel landmark-based, RBF-driven simulation for facial distraction surgery in children. METHODS: An individual’s dataset comprised of manually placed skin and bone landmarks on operated and unoperated regions. Surgical warps were produced for ‘older’ monobloc, ‘older’ bipartition and ‘younger’ bipartition groups by applying a weighted least-squares RBF fitted to the average landmarks and change vectors. A ‘normalisation’ warp, from fitting an RBF to craniometric landmark differences from the average, was applied to each dataset before the surgical warp. The normalisation was finally reversed to obtain the individual prediction. Predictions were compared to actual post-operative outcomes. RESULTS: The averaged change vectors for all groups showed skin and bone movements characteristic of the operations. Normalisation for shape–size removed individual asymmetry, size and proportion differences but retained typical pre-operative shape features. The surgical warps removed the average syndromic features. Reversing the normalisation reintroduced the individual’s variation into the prediction. The mid-facial regions were well predicted for all groups. Forehead and brow regions were less well predicted. CONCLUSIONS: Our novel, landmark-based, weighted RBF can predict the outcome for facial distraction in younger and older children with a variety of head and face shapes. It can replicate the surgical reality of composite bone and skin movement jointly in one model. The potential applications include audit of existing patient outcomes, and predicting outcome for new patients to aid surgical planning.