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, Pantel JT, PEDIA consortium, Krawitz P (2019) PEDIA: Prioritization of Exome Data by Image Analysis. bioRxiv 473306; doi.org/10.1101/473306. N= 679 retrospective patients diagnosed with monogenic disorders

UNLOCK PHENOTYPIC INSIGHTS TO IDENTIFY CAUSATIVE VARIANTS MORE EASILY
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.
SAVE TIME WITH NGP-BASED VARIANT PRIORITIZATION
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.
SHARE DATA SECURELY
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.
ACCESS UNPRECEDENTED PHENOTYPIC DATA
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.

Face2Gene User Community Includes Users From:

  • Using Face2Gene to reference all my department’s cases, share information with my colleagues and quickly look up relevant information in the London Medical Databases Online saves me hours of work every week and allows me to focus on my patients.

    Dr. Ibrahim Akalin

    Assoc. Prof. Ibrahim Akalin, MD, Medical Geneticist from the Istanbul Medeniyet University, Istanbul, Turkey

  • FDNA’s game-changing technology introduces an objective computer-aided dimension to the “art of dysmorphology”, transforming the analysis into an evidence-based science.

    Dr. Michael R. Hayden

    Chairman of FDNA’s Scientific Advisory Board & Steering Committee and Editor in Chief of Clinical Genetics

  • FDNA is developing technology that has the potential to help so many physicians and families by bringing them closer to a diagnosis- there are literally millions of individuals with unusual features around the world that lack a diagnosis and therefore lack information on natural history, recurrence risk and prevention of known complications.

    Dr. Judith G. Hall

    Professor Emerita of Pediatrics & Medical Genetics UBC & Children's and Women's Health Centre of BC

  • FDNA has been “right on the money”, providing me with relevant, accurate and insightful information for differential diagnoses.

    Dr. Cynthia J.R. Curry

    Professor of Pediatrics UCSF, Adjunct Professor of Pediatrics Stanford

  • I am excited to be a part of the FDNA community, promoting broad information sharing with my peers to amplify the scientific and clinical value of our community’s accumulated knowledge for the purpose of efficiently diagnosing individuals with rare genetic disorders.

    Dr. Karen W. Gripp

    Chief, Division of Medical Genetics A.I. duPont Hospital for Children

  • FDNA's idea of incorporating several dysmorphology resources (OMIM, GeneReviews), supported by their visual analytic technology, will be able to improve researching of genetic syndromes - all within a single mobile app.

    Dr. Chad Haldeman-Englert

    Assistant Professor Pediatrics at Mission Fullerton Genetics

  • Given the advancement of visual analytical technology, it’s about time Dysmorphology is supported with computational capabilities and moving this to mobile support, is simply the next logical step.

    Dr. Chanika Phornphutkul

    Associate Professor of Pediatrics Director, Division of Human Genetics Department of Pediatrics Warren Alpert Medical School of Brown University

  • Having an archive of cases easily accessible from my mobile device anytime and anywhere is a long-time unmet need.

    Dr. Lynne Bird

    Rady Children's Specialists of San Diego

  • FDNA's solution is a huge leap forward for dysmorphology. It saves me significant time when I’m evaluating patients in my clinic and provides me with insightful tools that help me generate a differential diagnosis.

    Dr. David A. Chitayat

    Head of the Prenatal Diagnosis and Medical Genetics Program at Mount Sinai Hospital, Toronto

  • Shortly after learning about Face2Gene, I’ve started to incorporate this amazing tool into my workflow. Soon enough, Face2Gene’s analysis flushed out references that I would not have considered for several of my patients, which turned out to be their correct diagnosis

    Dr. Zvi U. Borochowitz

    Chairman (Retired) of The Simon Winter Institute for Human Genetics at Bnai-Zion Medical Center, Technion-Rappaport Faculty of Medicine

  • The Unknown Forum from Face2Gene is a great community platform for exchanging opinions regarding undiagnosed cases. It is straightforward to use and safe for exchange of medical data, thanks to the efforts of its developers and to the involvement of geneticists worldwide.

    Dr. Oana Moldovan

    Clinical Geneticist at the Hospital Santa Maria, CHLN, Lisbon, Portugal