Nicole Fleischer

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.

This study identifies a recognizable craniofacial phenotype in 3q29 deletion syndrome using 2D facial photos and machine learning to distinguish cases from controls.

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.

The paper evaluates clinical features, flow cytometry, and facial image analysis to classify GPIBDs and identify gene-specific patterns, finding that facial recognition software is most effective for gene prediction.