Rickart Alexander J, Foti Simone, van de Lande Lara S, Wagner Connor, Schievano Silvia, Jeelani Noor Ul Owase, Clarkson Matthew J, Ong Juling, Swanson Jordan W, Bartlett Scott P, Taylor Jesse A, Dunaway David J
From the UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children.
Department of Computing, Imperial College London.
Plast Reconstr Surg. 2025 May 1;155(5):884e-892e. doi: 10.1097/PRS.0000000000011686. Epub 2024 Aug 20.
Advancements in artificial intelligence and the development of shape models that quantify normal head shape and facial morphology provide frameworks by which the outcomes of craniofacial surgery can be compared. In this work, the authors demonstrate the use of the swap disentangled variational autoencoder to assess changes after midfacial surgery objectively.
The model is trained on a data set of 1405 3-dimensional meshes of healthy individuals and syndromic patients, which was augmented using a technique based on spectral interpolation. Patients with a diagnosis of Apert or Crouzon syndrome who had undergone sub- or transcranial midfacial procedures using rigid external distraction had their results interpreted using this model as the point of comparison.
A total of 56 patients met the inclusion criteria: 20 with Apert syndrome and 36 with Crouzon syndrome. By using linear discriminant analysis to project the high-dimensional vectors derived by swap disentangled variational autoencoder onto a 2-dimensional space, the shape properties of Apert syndrome and Crouzon syndrome can be visualized in relation to the healthy population. In this way, the authors were able to show how surgery elicits global shape changes in each patient. To assess the regional movements achieved during surgery, the authors used a novel metric derived from the Mahalanobis distance to quantify movements through the latent space.
Objective outcome evaluation, which encourages in-depth analysis and enhances decision-making, is essential for the progression of surgical practice. The authors demonstrate how artificial intelligence has the ability to improve our understanding of surgery and its effect on craniofacial morphology.
人工智能的进步以及量化正常头部形状和面部形态的形状模型的发展提供了可用于比较颅面外科手术结果的框架。在这项研究中，作者展示了使用交换解缠变分自编码器来客观评估面中部手术后的变化。
该模型在1405个健康个体和综合征患者的三维网格数据集上进行训练，该数据集使用基于频谱插值的技术进行了扩充。对诊断为Apert综合征或Crouzon综合征且接受过使用刚性外部牵张的颅底或经颅面中部手术的患者，使用该模型解释其结果作为比较点。
共有56例患者符合纳入标准：20例Apert综合征患者和36例Crouzon综合征患者。通过使用线性判别分析将交换解缠变分自编码器导出的高维向量投影到二维空间中，可以将Apert综合征和Crouzon综合征的形状特征与健康人群进行可视化比较。通过这种方式，作者能够展示手术如何引起每位患者的整体形状变化。为了评估手术期间实现的区域移动，作者使用了一种从马氏距离导出的新指标来量化通过潜在空间的移动。
客观的结果评估对于鼓励深入分析并加强决策制定至关重要，这对于外科手术实践的进展必不可少。作者展示了人工智能如何有能力增进我们对手术及其对颅面形态影响的理解。
