The use of deep learning to undertake shape analysis of the complexities of
the human head holds great promise. However, there have traditionally been a
number of barriers to accurate modelling, especially when operating on both a
global and local level. In this work, we will discuss the application of the
Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon,
Apert and Muenke syndromes. Although syndrome classification is performed on
the entire mesh, it is also possible, for the first time, to analyse the
influence of each region of the head on the syndromic phenotype. By
manipulating specific parameters of the generative model, and producing
procedure-specific new shapes, it is also possible to simulate the outcome of a
range of craniofacial surgical procedures. This opens new avenues to advance
diagnosis, aids surgical planning and allows for the objective evaluation of
surgical outcomes.

深度学习在人头部的形状分析方面具有巨大潜力，本文介绍了使用 Swap Disentangled Variational Autoencoder (SD-VAE) 对 Crouzon、Apert 和 Muenke 综合征进行分类和研究，同时第一次分析了头部各个区域对综合征表型的影响，并通过调整生成模型的特定参数和生成特定程序的新形状，模拟了一系列颅颜面手术的结果，从而为进一步的诊断、手术规划和手术效果的客观评估开辟了新的途径。