PEDIA: prioritization of exome data by image analysis.

ABSTRACT:

Purpose

Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.

Methods

Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds.

Results

The additional use of similarity scores from computer-assisted an

SUBMITTER: Hsieh TC 

PROVIDER: S-EPMC6892739 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature