What is Next-Generation Phenotyping (NGP)?
Technologies that use artificial intelligence frameworks, such as FDNA’s DeepGestalt1, to capture, structure and analyze human phenotypic data to generate unique and comprehensive genomic insights and identify causative variants.
1 Gurovich et al (2019) Identifying rare genetic syndromes using deep learning. Nature Medicine. DOI:10.1038/s41591-018-0279-0
How NGP Improves Diagnostic Efficiency
The use of NGP in the NGS analysis process enables a dramatic increase in diagnostic efficiency and provides essential support for clinical correlation. The Prioritization of Exome Data Image Analysis (PEDIA)2 study found that the causative variant is ranked within the top-10 variants in more than 90% of cases when NGP and genomic scores are combined.
2 Hsieh TC, Mensah MA, […] Krawitz PM (2019) PEDIA: prioritization of exome data by image analysis. Genetics in Medicine. N= 679 retrospective patients diagnosed with monogenic disorders
A preliminary analysis of NGS cases found that integration of NGP improved prioritization of causative genes in up to 33% of cases, compared to existing methods. Overall, NGP ranking placed causative genes in the ≤ 5th rank in 50% of cases.
The clinical phenotype is critical for variant classification. The integration of NGP with NGS results in a new paradigm for superior genetic testing, thus reducing time spent, lowering testing costs, and dramatically increasing diagnostic yield and efficiency.
With our HIPAA-compliant, GDPR-compliant, and ISO 27001 certified platform, we facilitate the secure communication of protected health information (PHI) across more than 2,000 clinical and lab sites globally.
Our technologies’ unparalleled depth of phenotypic information, associated with more than 10,000 diseases and representing over 150,000 patients from 130 countries, is crowd-sourced from a global network of clinicians using Face2Gene.