发表机构
University of Augsburg; University of Bonn（奥格斯堡大学; 波恩大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FaceMesh2HPO框架利用临床医生注释及图像生成3D面部网格，基于PointNet训练含级联分类与特征消除的分层管道，结合多种数据实现面部表型分类，虽有成果但罕见叶节点性能受限，需改进策略增强实用性。
AI 中文摘要
FaceMesh2HPO是一个用于对与人类表型本体（HPO）对齐的面部表型描述符进行分类以支持临床诊断的框架。利用来自10种疾病（107个HPO术语）的124名临床医生的注释并结合非综合征对照，从二维图像生成3D面部网格（478个地标），并训练了基于PointNet的具有级联分类和特征消除的分层管道。最佳模型结合了3D网格、面部轮廓和人口统计元数据，AUROC在约0.55至约0.89之间，父节点性能高于叶节点。外部验证显示跨疾病的泛化性不同。结果表明，3D面部几何的层次建模能够实现可解释的、与本体相关的表型分类，不过罕见叶节点的性能仍然有限。需要改进数据多样性和特征选择策略以增强鲁棒性和临床实用性。
英文摘要
FaceMesh2HPO is a framework for classifying facial phenotypic descriptors aligned with the Human Phenotype Ontology (HPO) to support clinical diagnosis. Using annotations from 124 clinicians across 10 disorders (107 HPO terms) combined with non-syndromic controls, we generated 3D facial meshes (478 landmarks) from 2D images and trained a hierarchical PointNet-based pipeline with cascading classification and feature elimination. The best models, incorporating 3D meshes, facial outline, and demographic metadata, achieved AUROCs between ~0.55 and ~0.89, with higher performance at parent nodes than leaf terms. External validation showed variable generalizability across disorders. Results demonstrate that hierarchical modeling of 3D facial geometry enables interpretable, ontology-linked phenotype classification, though performance on rare leaf terms remains limited. Improved data diversity and feature selection strategies are needed to enhance robustness and clinical utility.
