I started using Face2Gene four years ago. What caught my attention then was not only the phenotypic facial analysis, but also the functionality of the Overview screen – where I fill out the patients’  info and the clinical notes. In many cases, I already had the patient chart including photos previously taken, so that I could easily use Face2Gene to capture the patient’s data, phenotype description and  measurements.

This saves me time during the consultation because Face2Gene does a calculation, provides all relevant links, and I don’t have to look in books for specific info and growth curves. Since I started using Face2Gene, it continues to improve and update, allowing me to use the image analysis technology more thoroughly. 

Today, Face2Gene is integrated into my daily practice and is an integral part of the anamnesis and workflow. I annotate the results of my patient’s physical examination directly in Face2Gene rather than in the medical record, and print the report from Face2Gene.

Check out how to incorporate Face2Gene into your workflow.

Face2Gene significantly helps us in our practice. Sometimes we have an idea of a possible syndrome, and Face2Gene confirms our hypothesis. And sometimes, the opposite happens. For example, I had a patient with Velo-Cardio-Facial Syndrome  (VCFS), but she didn’t have congenital heart disease, so I did not think this was the correct diagnosis. However, when I uploaded her photo to Face2Gene, the tool listed VCFS in a top rank. I still couldn’t believe it! But the genetic test came back indicating a microdeletion in chromosome 22, thus confirming the diagnosis.

I think artificial intelligence technology exists to help us as doctors. It will never replace the doctor in the clinic, but it will increasingly shorten the time to diagnosis and save us all much-needed time.

Want to master Face2Gene? Check out all the tutorials available

The post Using Face2Gene in the clinic even when the patient does not show clear dysmorphic features appeared first on FDNA™.

Dr. Christine Stanley, Head of Clinical Laboratory for WuXi NextCODE, US and CLIA medical director for Q&A Diagnostics, discusses next-generation phenotyping for improved variant interpretation through integration of Face2Gene LABS with WuXi NextCODE’s variant interpretation system.

According to Dr. Stanley, the ACMG has set standards on the classification and interpretation of genetic variants, including various levels of classifications as benign, unknown or pathogenic.

There are 28 lines of evidence used for classifying these variants.

“When you look at the phenotypic line of evidence, it wasn’t one that was utilized often […] but it really could be if we make it better and more complete,” she said. “In the ACMG publication, to use the phenotype as a line of evidence in variant scoring, you really need to have high clinical sensitivity, the patient must have a well-defined clinical presentation, the gene is not subject to a lot of variation, and the family history is consistent with the mode of inheritance of the disorder.”

“The phenotype information is really critical to performing whole exome and genome interpretation,” she continued.

Dr. Stanley goes on to explain that the phenotypic information allows Wuxi to utilize this phenotypic line of evidence in their variant classification and to help reclassify variants of unknown significance into the pathogenic category.

However, according to Dr. Stanley, phenotypic information is often lacking or not provided to support NGS interpretation.

She goes on to describe how WGS or WES produces a filtered list of variants that have clinical relevance, and that the report is static from what was known at the time. According to Dr. Stanley, “what we need are tools that allow for revisable reporting” because new information—such as new understanding of gene variations, or new patient symptoms—can become known down the road. This new information, ideally, can be used to reanalyze past genomics test results, impacting medical management, clinical research or patient support.

“We really need digital tools to get there,” she said. “We have taken the FDNA tool [Face2Gene] that captures the detailed clinical phenotype, and we’ve incorporated it into WuXi’s clinical sequence analyzer in order to do that real-time variant review.”

Dr. Stanley goes on to describe how the phenotype can be broadened to include other data, such as medical imaging, biometrics, clinical notes, and more, all pulled into Face2Gene and integrated with the variant interpretation.

She demonstrates how clinical users capture photos, anthropometric measurements, phenotypic features, and more within Face2Gene, and the application utilizes artificial intelligence to assist the user in further annotating the phenotype, resulting in a comprehensive phenotype for the patient. From there, the clinician can pass the phenotypic data securely to WuXi by selecting WuXi from the list of labs, resulting in the data being transferred into Wuxi’s clinical sequence analyzer.

Wuxi then reviews the gene list associated with the patient’s genetic sequence, scores the variants based on the ACMG classification criteria, and uses the clinical phenotype to match up to and highlight the variant results, or to highlight variants of unknown significance that relate to the phenotype to help determine if reclassification makes sense.

Watch the recording of the talk On Our Channel.

The post Dr. Christine Stanley: Face2Gene LABS at WuXi NextCODE: Phenotyping for Improved Variant Prioritization appeared first on FDNA™.

HISTORY

Since the 1960s, the field of medical genetics has been evolving rapidly with regard to phenotype delineation and analysis.

A disease phenotype requires a multi-faceted analysis. Besides defining the phenotype with diagnostic criteria, medical geneticists are also faced with the challenge of drawing lines along spectra of manifestations and within disease definitions, allowing for how symptoms may change over time.

To measure these variables, biochemical analysis, detailed histories, anthropometrics, standardized measures, and medical imaging can be applied in concert. As next-generation sequencing evolves the way the medical field examines patients’ genomes, next-generation phenotyping integrates these changes into the analysis of human health, maximizing the impact of new sequencing technologies.

“We’ve actually entered a new golden era of phenotyping,” Dr. Carey said.

PHENOTYPIC DOMAINS

Dr. Carey noted there are three domains to measure disease: diagnostic criteria (a.k.a. definition of the phenotype), the spectrum of manifestations and complications, and the natural history of how the phenotype changes over time.

SYNDROME DELINEATION

Syndrome delineation has three distinct parts, as Dr. Carey reviewed in Charlotte, N.C. The first, the physical examination, involves observations and descriptions of the patients. Then there are two levels of syndrome genesis to consider: formal (e.g., a shortage or malformation of a critical enzyme) and causal (e.g., the genetic source of the formal genesis, such as a deletion or de novo mutation.)

SYNDROME GROUPS & HETEROGENEITY

The broad definition of syndrome classification is misleadingly simple: simply group patterns of anomalies, with at least one being morphologic, thought to be etiologically related. Of course, in practice, this is very different than in theory; groups of syndromes, variant forms, subtypes, or related disorders can be hard to separate into discrete syndromes. Similar phenotypes can be caused by errors in different genes, or by multiple genes, and identical mutations can cause different phenotypes across patients.

“There are syndromes we know quite well that have 17 genes. There’s one gene that has 17 syndromes. Do we go with the gene or do we go with the phenotype?” Dr. Carey asked the audience.

THE AXIS MODEL

To solve these discrepancies, Dr. Carey proposed the use of the “axis model.” Axis I describes the clinical phenotype, Axis II describes the underlying molecular genetics, and Axis III describes non-genetic factors, like the environment.

“It’s never been adapted but I actually like it,” he said.

I would propose that even though you can’t [describe a syndrome in] 3 or 4 words, or less, Dr. Carey said of the sometimes wordy model. Despite its potential complication, its focus on phenotype keeps the naming mechanism patient-centered.

Watch the recording of the talk On our Channel.

The post Dr. John Carey: Delineating Genetic Syndromes and Next-Generation Phenotyping appeared first on FDNA™.

Starting in 1996, a select few clinicians began identifying a small group of patients with similar phenotypic features: Down-syndrome-like facial features, short stature, intellectual disability, cataracts and sensorineural hearing loss. Among these clinicians was Dr. Karen Gripp, who over the next two decades would become one of the namesakes for this MAF transcription factor related rare disease: Aymé-Gripp syndrome (AGS).

In her research, Dr. Gripp (the new Chief Medical Officer for FDNA) asked, “Does Aymé-Gripp syndrome have a facial phenotype that is recognizable using automated facial analysis?” Last week, she shared her investigation at the ACMG annual meeting.

Dr. Gripp used the Face2Gene RESEARCH application to create composite facial images for AGS and for Down syndrome, as well as for individuals with no suspected syndrome.

She ran the analysis twice, using new sets of cases for the Down syndrome and control cohorts for the second analysis to account for the “possibly subtle difference” in averaged facial images.

“There are twenty different images in those cohorts,” Dr. Gripp explained.

Dr. Gripp described the results provided by Face2Gene, including a multiclass comparison that reveals how well the system can successfully classify patients into the correct diagnosis based on facial analysis.

For each analysis, Face2Gene RESEARCH also provides statistics on the performance of the system.

“What you look at here is the mean accuracy for the analysis, and you have to always put that in perspective to the random chance for that particular analysis,” said Dr. Gripp.

Dr. Gripp’s use of the RESEARCH application is easily repeatable by any scientist on their “favorite syndrome.”

First, researchers should upload, to Face2Gene CLINIC, any patients (with at least one frontal facial photo) that they plan to use for research cohorts, being careful to clearly label [in the “case name” field] the study cohort that the patient should be added to, for example: “John Doe, Down Syndrome Cohort.” This makes it easier to find the relevant cases later when creating your study cohorts. If you are interested in analyzing the phenotype distribution for the disease, it is also important to thoroughly annotate a defined set of those phenotypes (present or absent) for every case. Otherwise, just a frontal facial photo for each case is sufficient for the analysis.

Once all the cases are added to Face2Gene CLINIC, you can move to the Face2Gene RESEARCH application by clicking “RESEARCH” in the top right corner of the app. There, you can create a new project, adding the cohorts you want to compare. To do this, select “add cohort,” and select the relevant cases from the list. Repeat for each cohort you want to create, making sure each cohort is roughly the same size.

“If you have a huge imbalance in the cohort size, that by itself introduces a bias,” Dr. Gripp mentioned.

Once cases are uploaded and cohorts are created, users can run their experiment with a click.

“You push the button, you get yourself a cup of coffee; it takes a little bit, literally a few minutes, for the system to run the analysis.”

When the analysis is complete, the user receives an email with summary results and can look in more detail at each item. Results include the sensitivity and specificity reported as the area under the curve of the Receiver Operating Characteristic (ROC) curve, and the p-value to measure significance.

All of the charts and visualizations are easily copied for use in papers and publications by the researcher.

For Dr. Gripp, using the Face2Gene application resulted in the addition of a new syndrome within Face2Gene, paving the way for clinicians globally to better recognize the AGS phenotype in patients through use of  Face2Gene–and any researcher with an internet connection can now do the same for other diseases.

Watch the recording of the talk On our Youtube Channel.

The post Dr. Karen Gripp: Face2Gene RESEARCH for Deep Phenotyping of Novel Syndromes appeared first on FDNA™.

Dr. Carey. a highly-practiced clinician, currently at the University of Utah’s Department of Medical Genetics, kicked off the webinar with a comprehensive background on phenotyping, including a discussion of the important role phenotyping plays in the process of diagnosing a patient.

Dr. Carey noted colleagues’ worries that there would be a decline of phenotypic analysis as next-generation sequencing (NGS) rises in popularity and accessibility, but went on to agree with the sentiments from a 2012 paper by Hennekam & Bisecker that, in fact, this new era of genome sequencing has led us to enter a “new epoch of phenotyping.” Rather than replacing the need for phenotyping, NGS has instead led to a new way of phenotyping.

Over the last few years, Dr. Carey noted there has been a “proliferation of resources which speaks to this notion of deep phenotyping.” Among such resources is FDNA’s Face2Gene technology. Using case studies as examples, Dr. Carey demonstrates the benefit Face2Gene can play in picking up on subtle facial patterns and, in turn, deciding on testing.

“We [clinicians] would benefit by having expanded knowledge and tools truly at our fingertips, but there is no question that the patients will benefit with the increased chance of making a diagnosis, or at least helping us in our genome analyses”

Dr. Stanley built on this point by sharing insights into the importance of phenotyping from a lab perspective. Drawing on her extensive background, most recently as the Head of Clinical Labs, US at WuXi NextCODE, Dr. Stanley was able to provide a thorough look into what exactly is required for a successful genetic test. Previously, phenotypic information was not required when ordering testing, but now, “the justification for testing is dependent on a complete and accurate clinical intake.”

According to Dr. Stanley, the clinical phenotype is very important as a line of evidence in variant classification. She goes on to say that “the more limited the phenotype, the greater the risk the disease causing variant will be filtered out of the data set.” In order to avoid the accidental omittance of the causal variant from the report, Dr. Stanley suggests increasing the use of the PP4 line of evidence—which is associated with the patient phenotype—in testing, possibly by way of tools that help to incorporate phenotypic information into clinical reports.

“The phenotypic information combined with the gene sequence information is a powerful combination.”

According to Dr. Stanley, with the use of FDNA’s Face2Gene technology, a “dynamic feedback loop of clinical symptoms & genomic information can be achieved,” which leads to improved variant prioritization and speedier diagnosis. Ultimately, Dr. Stanley summarized that “phenotyping is critical for diagnostics and diagnostics are critical for patient management.”

“Every aspect of healthcare can be impacted by understanding the phenotype.”

Listen in as Dr. Carey and Dr. Stanley share their respective clinical and lab perspectives about genomic medicine and NGS ushering in a new era of phenotyping.

Continue the Discussion With These Thought Leadership Talks at ACMG

Visit FDNA (Booth #1015) to learn more

Delineating Genetic Syndromes and Next-Generation Phenotyping

John C. Carey, MD, MPH
Professor, Department of Pediatrics, University of Utah
Emeritus Editor in Chief, American Journal of Medical Genetics
Thursday, 4/12 10:15AM

Face2Gene RESEARCH for Deep Phenotyping of Novel Syndromes

Karen Gripp, MD
Chief, Division of Medical Genetics, A.I. DuPont Hospital for Children
Thursday, 4/12 11:30AM

Face2Gene LABS at WuXi NextCODE: Phenotyping for Improved Variant Prioritization

Christine Stanley, PhD, FACMG
Head of Clinical Laboratory, US, WuXi NextCODE
Medical Director, QNA Diagnostics
Thursday, 4/12 3:45PM

Next-Generation Phenotyping in the Era of Next-Generation Sequencing

Dekel Gelbman
Chief Executive Officer, FDNA
Friday, 4/13 10:15AM

The post Redefining Phenotyping for Clinical Advancements and Variant Prioritization appeared first on FDNA™.

HIMSS Conference, Microsoft booth

Thursday, March 7 3:40 PM PST

Las Vegas, Nevada

As over 40,000 health IT professionals, clinicians, executives, and vendors from around the world gather at this year’s HIMSS Annual Conference & Exhibition, speaker Anthony Antonuccio, VP of Product at FDNA shares FDNA’s experience with one of the leading clinics, Greenwood Genetic Center (GGC) in his talk, Precision Medicine Through Next-Generation Phenotyping—A Customer’s Journey. Discover how integration of FDNA’s facial analysis technology into GGC’s genetic evaluation workflow has led to the expansion of knowledge around countless rare diseases by the evaluation of over 40 years of data and over 40,000 patients seen.

FDNA’S Face2Gene TECHNOLOGY

FDNA is the developer of Face2Gene, a clinical suite of phenotyping applications that facilitates comprehensive and precise genetic evaluations. Precision medicine aims to personalize healthcare, factoring in individuals’ traits—genetics, lifestyle, etc.—to develop targeted approaches to diagnosis, treatment, and prevention for patients. Face2Gene uses facial analysis, deep learning, and artificial intelligence to transform big data into actionable genomic insights to improve and accelerate diagnostics and therapeutics. With the world’s largest network of clinicians, labs, and researchers creating one of the fastest-growing and most comprehensive genomic databases, FDNA is changing the lives of rare disease patients. For more information, visit www.FDNA.com.

GREENWOOD GENETIC CENTER

The Greenwood Genetic Center has recently partnered with FDNA to collaborate using next-generation phenotyping technology, Face2Gene. With the aid of FDNA’s facial analysis and artificial intelligence technology, analysis of nearly 80,000 cases from Greenwood Genetic Center will contribute to the ever-expanding database of rare disease information. With the large influx of cases from GGC now analyzed by Face2Gene, insights for a myriad of undiagnosed patients and syndrome-related features have contributed to advancements in the research of rare diseases. For more information, visit www.GGC.com.

The post Happening at HIMMS: Precision Medicine Through Next-Generation Phenotyping—A Customer’s Journey appeared first on FDNA™.

In a packed auditorium at Mexico City’s CENTRO University, FDNA’s Senior Director of Marketing, Jeff Daniels, announced the launch of the Genomics Collaborative. This initiative prompts clinicians, patients, advocates and researchers to join forces in an effort to accelerate breakthroughs in precision medicine. The Genomics Collaborative allows participants to access FDNA’s deep learning and next-generation phenotyping (NGP) technology to conduct research for the benefit of patients and health care practices.

Leaders and innovators from around the globe were gathered to learn about breakthroughs from the creative visionaries behind them at the Demand Solutions summit. Hosted by the renowned Inter-American Development Bank (BID), CNN and Entrepreneur Magazine, the event drew over 500 leaders and innovators and showcased thought leaders from startups, including Airbnb and several wearable technology and gaming companies.

During his presentation, Daniels highlighted FDNA’s use of artificial intelligence and facial analysis technologies to help patients around the world. Using the Yellow Brick Road Project’s “Morgan’s Story,” as a backdrop, Daniels shared how the struggle of two little girls and their families is faced by millions of people globally when searching for medical answers.

“With FDNA’s strength being in pattern recognition and artificial intelligence, we thought others might want to access our technology to support their research efforts as well. That’s why we are launching the Genomics Collaborative and announcing projects with hospitals, labs, countries who have genomics projects, patient advocacy groups, and dozens of other types of researchers, just like Morgan and Lillie’s group, the Yellow Brick Road Project. We intend to expand use of this technology to every disease, and to phenotypes beyond facial analysis—like voice, video, metabolites, analysis of clinical notes, and more.”

As part of the Genomics Collaborative, FDNA works with partners to design the future of health, together. By compiling and analyzing case data, such as patient photos, biometric data, clinical notes and disease history, scientists will be able to make breakthroughs in disease understanding. Unlocking the secrets to our genetic makeup is the key to a healthier world. By uncovering the genetic components of diseases, we can shorten the diagnostic odyssey, enable greater and earlier recognition of these conditions in the medical community, and lead the way to more personalized and effective therapy.

Daniels discussed FDNA’s accomplishments over the past few years in this space.

“This is Face2Gene,” said Daniels, as he shared a photograph of a doctor scanning a patient with her mobile phone. “Face2Gene is for healthcare providers, and it works on any computer, tablet or mobile device […] A doctor can take a photo of her patient, or list other patient features, and Face2Gene will produce a list of relevant diseases for the doctor to consider. For thousands of diseases, the technology can provide meaningful insights before symptoms even begin.”

“What’s more,” Daniels continued, “Face2Gene learns from diagnosed cases from its users, getting smarter every day. It’s called deep learning technology. Doctors from over 2,000 institutions in 130 countries worked to train this technology, including hundreds here in Mexico. It already recognizes features and genetic data for over 10,000 diseases.

“Instead of waiting years to get a diagnosis, like Morgan and Lillie did, like many of you or your loved ones have, doctors can tap into the collective experience of thousands of doctors globally to facilitate personalized medicine, getting us answers sooner.”

Get Involved. Join the Genomics Collaborative>>

The post FDNA Announces Genomics Collaborative® at a Global Innovation Summit appeared first on FDNA™.

Advocacy: “Nobody needs to wait a single moment before starting to improve the world.”

Every month during the 2017 Year of Discovery, clinicians, labs and patients focused on a specific disease category to build a repository of rare disease “big data.” Sponsors including GeneDx and Blueprint Genetics helped drive patient case uploads by matching each case loaded with a charitable donation to patient advocacy charities. As a result of the increased community support, Face2Gene improved its recognition and understanding of the phenotype of dozens of diseases, including Sanfilippo Syndrome and a variety of craniosynostoses.

The Year of Discovery launched on 2017’s World Rare Disease Day. Look out for another exciting announcement during this year’s February 28 event.

Integrations: Better together

Across the globe, genetics labs including Ambry Genetics, GeneDx and Blueprint Genetics are integrating Face2Gene into their workflow. With the addition of these labs, over 70 percent of clinical geneticists worldwide are able to harness the power of artificial intelligence and computer vision to highlight genetic variants correlated with underlying diseases, increasing the diagnostic yield of genetic testing for their patients. Dramatically, we should say: preliminary results from the PEDIA study indicate adding phenotypic data and facial analysis can triple the diagnostic yield from genetic sequencing alone, in some cases.

We’re building relationships internally, too: in 2017, the FDNA Team grew by 23 people, full and part time, magnifying the human brain power behind the Face2Gene AI.

Product expansion: Suite, suite progress

These integrations are made possible by the latest additions to the Face2Gene suite. Genetics labs can use Face2Gene’s new LABS capability to securely transmit next-generation phenotyping and facial analysis insights directly into the molecular interpretation pipeline to increase diagnostic speed and accuracy.

Using the Face2Gene RESEARCH app, clinicians can use de-identified information to create cohorts and test hypotheses. Many abstracts and publications are resulting.

In the News: Our ears are burning

Artificial intelligence has been a hot topic in the news this year. Cygnis Media highlighted a range of ways machine learning and artificial intelligence are changing modern medicine, from personal trainers in biosensing earbuds to eye-tracking technology that can identity Autism Spectrum Disorders early. A special edition of TIME investigated advancements from smart-home thermostats to self-driving cars, and even how etiquette changes when we interact with machines.

FDNA found itself mentioned in dozens of high-profile articles, including the cover story of Fortune’s future issue, The Doctor Will See You Now, and the Economist’s September issue, What Machines Can Tell From Your Face. WIRED called Face2Gene “a breakthrough method… the most promising to deliver AI’s 50-year-old promise to revolutionize medicine.”

Partnerships: Left side, strong side

FDNA’s network of databases, researchers, and clinicians continued to grow this year. POSSUMWeb, a leading dysmorphology database, announced in October that it would partner with FDNA for integration and distribution of its over 40,000 images through Face2Gene LIBRARY. The Greenwood Genetic Center (GGC) announced its intention to analyze thousands of undiagnosed cases with Face2Gene, with the goal of finding new leads for unsolved cases. And at the end of 2017, Vanderbilt University Medical Center announced their use of Face2Gene as part of the NIH Undiagnosed Disease Network.

FDNA partnered with Cincinnati Children’s Hospital and used photos of patients with confirmed PACS1 diagnoses to train Face2Gene on disease-related features and as a result, the system reached 96 percent performance.

Future Plans: To boldly go

With all this momentum, FDNA is excited to launch a new effort to use FDNA’s artificial intelligence and deep learning technology to develop precision medicine approaches for diagnosing and treating disease. Details to come on World Rare Disease Day.

The post 2017 Year in Review appeared first on FDNA™.

At this year’s 22nd Annual Drug Delivery Partnership meeting, a new approach to the pharma value chain is in discussion. Speaker Barry Frankel explores the power of precision medicine for pharma in his talk, Enabling Drug Development: Artificial Intelligence, Phenotypes and Genomics Integrated for Precision Medicine. Precision medicine aims to personalize healthcare, factoring in individuals’ traits—genetics, lifestyle, etc.—to develop targeted approaches to diagnosis, treatment, and prevention for patients.

The incorporation of genomic information into pharmaceutical development is on the rise; Frankel highlights that genomics adoption is now faster than Moore’s Law*. However, the industry is still facing barriers to full-on adoption. Drugs based on genetic targets have a success rate 1.5-2X better than their counterparts that don’t include genetic information, but identifying the genetic variant causing a disease is like trying to find a needle in a haystack.

In steps FDNA and their deep learning technology. The technology captures physiological information, biometric data—such as facial photos—and clinical notes, curated in a proprietary database, to identify disease-causing genetic variants with higher accuracy in a process called “next-generation phenotyping” (NGP).

NGP is the use of computational techniques to integrate phenotypic data into the analysis of human health, including the capturing, structuring, and interpretation of complex clinical information. The correlations found between phenotypic data and genomic data improve and accelerate targeted diagnostics, therapeutics development, and patient care.

“We estimate that the addition of phenotypic features increases the diagnostic yield to about 60% (from 25% without).  When adding facial analysis, FDNA’s technology, to that process, the diagnostic yield increases to more than 85%.”  Dr. Peter Krawitz University Hospital Bonn, Germany, Principal Investigator of PEDIA

NGP has the capacity to complement NGS across the entire Pharma chain, from discovery to development to commercialization. NGP helps researchers identify and target disease biomarkers, enable earlier and more targeted patient diagnosis for trial recruitment, and personalize therapeutic approaches.

Interested in learning more about using NGP for the benefit of your development and commercialization activities? Learn more at www.FDNA.com

*Source:  National Institute of Health, National Human Genome Research Institute (7/17), Biology Reference, Illumina

The post Drug Delivery Partnership: Precision Medicine Drives Discovery, Development and Commercialization in Pharma appeared first on FDNA™.

As part of FDNA’s Year of Discovery, FDNA collaborated with the National Foundation for Ectodermal Dysplasias, sponsored by GeneDx, to research and promote awareness of Skin Disorders throughout the month of December.

About Skin Disorders

Abnormalities of the hair, skin, sweat glands, nails, and teeth are frequently associated with various genetic diseases. The list of rare skin disorders is lengthy, with a wide range of severity and symptoms. Often, the features associated with these conditions are present at birth or develop within the first few years of life, though detection during pregnancy is also possible. Some of the associated features of many skin disorders include:

• Skin lesions
• Abnormal or missing nails
• Sparse hair and eyebrows
• Absent teeth or delayed dentition, and more.

Ectodermal Dysplasias

Nearly 200 types of syndromes are classified under a category of skin disorders known as “Ectodermal Dysplasias,” identified by the specific combination of symptoms that are present in the given individual. One in every 10,000 children per year are born with a form of Ectodermal Dysplasia, such as Coffin-Siris syndrome, several forms of Ehlers-Danlos syndrome, and Trichorhinophalangeal syndromes. More information about these conditions can be found at the National Foundation for Ectodermal Dysplasias.

Expanding Resources through Technology

Typically, the process of diagnosing and treating rare skin disorders involves consulting various clinical specialists. With advancements in technology, clinicians are able to easily access and utilize improved resources, which can aid in the tests and evaluations used to identify and gain a greater understanding of different syndromes. Thanks to the collection of many facial images of patients diagnosed with these rare diseases, FDNA’s platform, Face2Gene Suite, can learn valuable information on their clinical presentation. In result, we have been able to create and validate a model for the facial patterns that are common among these individuals. Below you will find some of the validated facial masks of skin disorders generated by Face2Gene.

The above, along with myriad other syndromes, are available
for patient evaluations via Face2Gene CLINIC.

The post Skin Disorders Discoveries in the Year of Discovery appeared first on FDNA™.