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A 3D Clinical Face Phenotype Space of Genetic Syndromes Using a Triplet-Based Singular Geometric Autoencoder

ISSS Journal of Micro and Smart Systems(2025)

Vrije Univ Brussel | Katholieke Univ Leuven | Univ Athens | Curtin Univ | Univ Colorado | Univ Calif San Francisco | Univ Oxford | Univ Calgary | KU Leuven

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Abstract
Clinical diagnosis of syndromes benefits strongly from objective facial phenotyping. This study introduces a novel approach to enhance clinical diagnosis through the development and exploration of a low-dimensional metric space referred to as the clinical face phenotypic space (CFPS). As a facial matching tool for clinical genetics, such CFPS can enhance clinical diagnosis. It helps to interpret facial dysmorphisms of a subject by placing them within the space of known dysmorphisms. In this paper, a triplet loss-based autoencoder developed by geometric deep learning (GDL) is trained using multi-task learning, which combines supervised and unsupervised learning approaches. Experiments are designed to illustrate the following properties of CFPSs that can aid clinicians in narrowing down their search space: a CFPS can 1) classify syndromes accurately, 2) generalize to novel syndromes, and 3) preserve the relatedness of genetic diseases, meaning that clusters of phenotypically similar disorders reflect functional relationships between genes. The proposed model consists of three main components: an encoder based on GDL optimizing distances between groups of individuals in the CFPS, a decoder enhancing classification by reconstructing faces, and a singular value decomposition layer maintaining orthogonality and optimal variance distribution across dimensions. This allows for the selection of an optimal number of CFPS dimensions as well as improving the classification capacity of the CFPS, which outperforms the linear metric learning baseline in both syndrome classification and generalization to novel syndromes. We further proved the usefulness of each component of the proposed framework, highlighting their individual impact. From a clinical perspective, the unique combination of these properties in a single CFPS results in a powerful tool that can be incorporated into current clinical practices to assess facial dysmorphism.
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3D Shape Analysis,Clinical Genetics,Computer-aided Diagnosis,Deep Phenotyping,Geometric Deep Learning,Precision Public Health
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要点】:本研究开发并探索了一个用于增强临床诊断的三维临床面部表型空间(CFPS),通过基于几何深度学习的成对损失自编码器实现,该自编码器采用多任务学习结合监督与无监督学习。

方法】:研究采用了一种基于成对损失的几何深度学习自编码器,通过多任务学习进行训练。

实验】:实验表明CFPS能准确分类综合征,泛化到新的综合征,并保留遗传疾病之间的相关性。模型由三个主要部分组成:基于GDL的编码器,优化CFPS中个体群体的距离;增强分类的解码器,通过重建面部;以及保持正交性和最优方差分布的奇异值分解层。这使得可以选择最优的CFPS维度并提高其分类能力。使用的数据集未在文中明确提及,但实验旨在展示CFPS的性质。