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

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

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 ma...

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Bibliographic Details
Main Authors: Soha S. Mahdi, Eduarda Caldeira, Harold Matthews, Michiel Vanneste, Nele Nauwelaers, Meng Yuan, Giorgos Bouritsas, Gareth S. Baynam, Peter Hammond, Richard Spritz, Ophir D. Klein, Michael Bronstein, Benedikt Hallgrimsson, Hilde Peeters, Peter Claes
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
3D Shape Analysis
Clinical Genetics
Computer-aided Diagnosis
Deep Phenotyping
Geometric Deep Learning
Precision Public Health
Online Access:https://ieeexplore.ieee.org/document/10818677/
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