Department of Anatomy, University of Pretoria, Pretoria, South Africa.
Laboratory for Imaging Genetics, Department of Human Genetics, Katholieke Universiteit, Leuven, Belgium.
J Prosthodont. 2024 Jul;33(6):565-573. doi: 10.1111/jopr.13746. Epub 2023 Oct 4.
Facial disfigurement may affect the quality of life of many patients. Facial prostheses are often used as an adjuvant to surgical intervention and may sometimes be the only viable treatment option. Traditional methods for designing soft-tissue facial prostheses are time-consuming and subjective, while existing digital techniques are based on mirroring of contralateral features of the patient, or the use of existing feature templates/models that may not be readily available. We aim to support the objective and semi-automated design of facial prostheses with primary application to midline or bilateral defect restoration where no contralateral features are present. Specifically, we developed and validated a statistical shape model (SSM) for estimating the shape of missing facial soft tissue segments, from any intact parts of the face.
An SSM of 3D facial variations was built from meshes extracted from computed tomography and cone beam computed tomography images of a black South African sample (n = 235) without facial disfigurement. Various types of facial defects were simulated, and the missing parts were estimated automatically by a weighted fit of each mesh to the SSM. The estimated regions were compared to the original regions using color maps and root-mean-square (RMS) distances.
Root mean square errors (RMSE) for defect estimations of one orbit, partial nose, cheek, and lip were all below 1.71 mm. Errors for the full nose, bi-orbital defects, as well as small and large composite defects were between 2.10 and 2.58 mm. Statistically significant associations of age and type of defect with RMSE were observed, but not with sex or imaging modality.
This method can support the objective and semi-automated design of facial prostheses, specifically for defects in the midline, crossing the midline or bilateral defects, by facilitating time-consuming and skill-dependent aspects of prosthesis design.
面部畸形可能会影响许多患者的生活质量。面部假体通常作为手术干预的辅助手段，有时可能是唯一可行的治疗选择。传统的软组织面部假体设计方法既耗时又主观，而现有的数字技术则基于患者对侧特征的镜像，或使用可能不容易获得的现有特征模板/模型。我们旨在通过支持客观和半自动的面部假体设计，为中线或双侧缺陷修复提供帮助，因为这些部位没有对侧特征。具体来说，我们开发并验证了一种统计形状模型（SSM），用于从面部任何完整部分估计缺失的面部软组织部分的形状。
从无面部畸形的南非黑人样本（n=235）的计算机断层扫描和锥形束计算机断层扫描图像中提取的网格构建了一个 3D 面部变异的 SSM。模拟了各种类型的面部缺陷，并通过每个网格对 SSM 的加权拟合自动估计缺失部分。使用颜色图和均方根（RMS）距离将估计区域与原始区域进行比较。
一个眼眶、部分鼻子、脸颊和嘴唇的缺陷估计 RMS 误差（RMSE）均低于 1.71 毫米。全鼻、双眶缺陷以及小和大复合缺陷的误差在 2.10 至 2.58 毫米之间。观察到年龄和缺陷类型与 RMSE 存在统计学显著关联，但与性别或成像方式无关。
该方法可以支持中线、中线交叉或双侧缺陷的面部假体的客观和半自动设计，通过简化假体设计中耗时且依赖技能的方面来实现。
