Analyzing Your Patients with NGP

Next-Generation Phenotyping

Face2Gene™ is  proud to present different algorithms and visualizations, to provide you with state-of-the-art NGP analysis for your patients

 

* During the Beta stage , the magnitude of similarities is not calibrated and does not allow comparison between the different algorithms*

DeepGestalt™

The input photo is first pre-processed to achieve facial detection, landmark detection and alignment.

After preprocessing, the input image is cropped into facial regions. Each region is fed into a Deep Convolutional Neural Network (DCNN) to obtain a softmax vector indicating its correspondence to each syndrome in the model. The output vectors of all regional DCNNs are then aggregated and sorted to obtain a final ranked list of genetic syndromes — the 30 syndrome matches displayed in Face2Gene™ CLINIC’s RARE tab. More details in Nature Medicine.  Current availability: web, iOS & Android app.

 

FeatureMatcher

Following the Human Phenotype Ontology terms, this algorithm allows a phenotype-based syndrome prioritization. In Face2Gene™ ‘s RARE tab, these results are combined with DeepGestalt™’s results to provide a ranked list of syndromes. More details on how this was used in PEDIA algorithm, in Genetics in Medicine. FeatureMatcher is also available in LIBRARY and Face2Gene™ LABS. Current availability of CLINIC: web, iOS & Android app.

 

GestaltMatcher™ Beta

Based on the DeepGestalt™ framework a “Clinical Face Phenotype Space” is created, such that the distance between photos defines syndromic similarity. This is listed in Face2Gene™ CLINIC’s ULTRA-RARE tab, allowing patient photos to be matched to a molecular diagnosis even when the disorder was not part of the training set. The output is displayed in two parallel lists, the first one ranking the matched patients, and the second one, ranking the syndromes of these matched patients.

Similarities among patients with previously unknown disease genes can also be detected, and these are displayed in the UNDIAGNOSED tab in Face2Gene™ CLINIC. More details in Nature Genetics. Current availability: web app only.

 

Facial D-score Beta

Based on the algorithms described above we built descriptors to differentiate between 2 classes of frontal facial photos: images of patients diagnosed with a rare genetic disease and presenting a facial dysmorphia, and an equivalently sampled second class of images of unaffected individuals. The tool currently supports pediatric-aged patients, mostly of Caucasian ethnicity. While more details will soon be published, you can read how this was used in a preliminary study.  Current availability: web and mobile apps.

 

GeneSearch

Combining DeepGestalt™, FeatureMatcher and extensive gene mutation databases, this algorithm enables correlation of NGS results with all the phenotypic information captured by the above mentioned algorithms in Face2Gene™ CLINIC, thus facilitating the more accurate and efficient interpretation of genomic variant profiles. More details on how this was used in the PEDIA algorithm, in Genetics in Medicine. Current availability: Face2Gene™ LAB web only.

 

Text2Phenotype Beta

Text-mined phenotype annotation allows extracting the features from clinical notes directly into Human Phenotype Ontology terms. Available also as dictated text, these terms can be then analyzed by the FeatureMatcher algorithm and combined with DeepGestalt™ to render a ranked list of syndrome matches listed in Face2Gene™ CLINIC’s RARE tab. Current availability: Face2Gene™ CLINIC web app and Face2Gene™ LAB.

 

t-SNE visualization

Shows a 2D projection of the case image into a “Clinical Face Phenotype Space” as compared to relevant syndromes. There are currently 2 TSNE visualizations available in Face2Gene™ CLINIC: the first one providing a full analysis for your patient, and a second one, for the matched undiagnosed patient. The first TSNE displays the projection of your patient’s photo as compared to the top-10 syndromes analyzed by the DeepGestalt™ and GestaltMatcher™ algorithms*. The second TSNE, in the UNDIAGNOSED tab, projects the matched photo as compared to the top-10 syndromes resulting from the GestaltMatcher algorithm analysis. Current availability: Face2Gene CLINIC web app only.

 

Masks/ composite images

The facial descriptors can also be graphically displayed as a two-dimensional model of the face specific to the particular syndrome of interest. These composites are created taking an average of the images that participated in the training of a particular syndrome.

Current availability: Face2Gene™ CLINIC web, iOS & Android apps

 

Heatmaps

A graphical heatmap can be applied to visualize the degree of similarity between the two descriptors being compared, namely, the descriptor of the patient’s photo being analyzed and the composite image of the syndrome being studied.

Current availability: Face2Gene CLINIC web, iOS & Android apps

 

 

* During the Beta stage, the magnitude of similarities is not calibrated. Please do not compare between the magnitudes of results of the different algorithms.

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