FaceKit：罕见病中可解释面部表型分析、合成图像生成与隐私分析的工具包

中文摘要 AI 辅助

许多罕见遗传疾病与可识别的颅面特征相关。然而，描述面部形态的传统方法主要依赖于定性的临床观察和自由文本描述，这些方法往往具有主观性、非标准化，并且难以在不同观察者和机构之间复现。尽管人类表型本体论（HPO）提供了用于描述面部特征的控制术语，但这些术语通常是分类性的而非定量性的，并且可能因检查者的经验和解释而有所不同。在此，我们提出了FaceKit，一个用于从正面面部照片进行定量面部表型分析的计算框架。FaceKit提取面部标志点的标准化测量值，并衍生出120个形态学特征，然后报告代表偏离群体参考分布程度的特征级z分数。参考分布基于涵盖不同祖先群体的FairFace数据集构建。我们在GestaltMatcher数据库的一个精选子集上评估了FaceKit，该子集涵盖50个罕见病队列。除了定量面部分析外，FaceKit还包括合成面部图像生成，以支持罕见病模型开发和数据增强。我们还进行了隐私评估，以评估合成图像是否揭示了真实患者照片中的可识别信息，并可能损害患者隐私。在疾病案例研究中，FaceKit衍生的定量测量捕获了与罕见遗传疾病相关的已知面部特征，并为临床表型分析提供了客观支持。总之，这些结果确立了FaceKit作为定量表型分析的有用工具，并具有改善罕见病诊断、支持基因型-表型研究以及在不同患者群体中实现更可重复的临床表征的潜力。

英文摘要

Many rare genetic diseases are associated with recognizable craniofacial features. However, traditional approaches for describing facial morphology rely largely on qualitative clinical observation and free-text descriptions, which are often subjective, non-standardized, and difficult to reproduce across observers and institutions. Although the Human Phenotype Ontology (HPO) provides controlled terms for describing facial features, these terms are typically categorical rather than quantitative and may vary depending on examiner experience and interpretation. Here, we present FaceKit, a computational framework for quantitative facial phenotyping from frontal facial photographs. FaceKit extracts standardized measurements of facial landmarks and derived 120 morphological features, then reports feature-level z-scores representing deviation from population reference distributions. The reference distributions are built from the FairFace dataset spanning diverse ancestral groups. We evaluated FaceKit on a curated subset of the GestaltMatcher Database covering 50 rare-disease cohorts. In addition to quantitative facial analysis, FaceKit includes synthetic facial image generation to support rare disease model development and data augmentation. We also performed privacy evaluation to assess whether synthetic images reveal identifiable information from real patient photographs and could compromise patient privacy. Across disease case studies, FaceKit-derived quantitative measurements captured known facial features associated with rare genetic disorders and provided objective support for clinical phenotyping. Together, these results establish FaceKit as a useful tool for quantitative phenotyping, and has the potential to improve rare disease diagnosis, support genotype-phenotype studies, and enable more reproducible clinical characterization across diverse patient populations.

发表机构

• University of Pennsylvania（宾夕法尼亚大学）
• Columbia University（哥伦比亚大学）
• New York Genome Center（纽约基因组中心）
• Havard Westlake School（哈佛西湖学校）
• University Hospital Bonn（波恩大学医院）
• Rheinische Friedrich-Wilhelms-Universitat Bonn（波恩大学）
• Harvard Medical School（哈佛医学院）

机构由 AI 辅助整理，请以论文原文为准。