Tom Kamphans

The paper introduces the GestaltMatcher Database (GMDB), a global facial phenotypic reference for rare diseases, showing improved NGP performance with diverse ancestry data.

GestaltMatcher improves diagnosis of ultra-rare genetic disorders by matching facial phenotypes to molecular diagnoses using a deep learning model trained on a large dataset of patient photographs.

The paper introduces an AI-driven method that uses facial images to prioritize exome data, achieving 99% top-10 accuracy in identifying disease-causing genes.

This paper evaluates deep learning for syndrome recognition in metabolic disorders, finding improved accuracy with larger training sets but no significant effect from ethnicity or sex.