GestaltGAN: synthetic photorealistic portraits of individuals with rare genetic disorders.

ABSTRACT: The facial gestalt (overall facial morphology) is a characteristic clinical feature in many genetic disorders that is often essential for suspecting and establishing a specific diagnosis. Therefore, publishing images of individuals affected by pathogenic variants in disease-associated genes has been an important part of scientific communication. Furthermore, medical imaging data is also crucial for teaching and training deep-learning models such as GestaltMatcher. However, medical data is often sparsely available, and sharing patient images involves risks related to privacy and re-identification. Therefore, we explored whether generative neural networks can be used to synthesize accurate portraits for rare disorders. We modified a StyleGAN architecture and trained it to produce artificial

SUBMITTER: Kirchhoff A 

PROVIDER: S-EPMC11894188 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature