Jean Tori Pantel

Bone2Gene uses deep learning to classify rare bone diseases from hand radiographs, achieving high accuracy for distinct disorders but lower for overlapping ones.

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

The paper evaluates a structured diagnostic framework for ultra-rare diseases, showing improved genetic diagnoses and novel gene-disease associations using exome sequencing and AI tools.

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 models how population expansion affects recessive disorder prevalence and mutation load, showing that random mating leads to a temporary decrease in disease incidence but increased mutation load.

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.