Dr. Bordest’s passion for genetics began early in his medical career. “Since medical school, I have always been interested in rare and atypical diseases,” he recalls. Despite a lack of formal training in medical genetics during his early years, he sought out every opportunity to learn. His journey eventually led him to discover Face2Gene, a tool that has since become an integral part of his practice.

He first encountered Face2Gene during his residency. “I had a colleague who was very enthusiastic about it, and he influenced me to start using it. At first, I was skeptical,” he admits. “But as I tested it on different cases, I realized the tool was providing information about potential conditions that, after clinical evaluation, aligned with confirmed diagnosis. That built my confidence in the technology.”

The Diagnostic Bottleneck and the Role of Pediatricians

The diagnostic odyssey for patients with rare genetic disorders often takes an average of four years, though in many cases, it takes much longer. Dr. Bordest sees several reasons for this delay. “Primary care physicians are overwhelmed with common conditions – growth and development issues, infections, and routine pediatric concerns. Rare diseases often go unnoticed in this busy environment.”

Another challenge is the lack of specialists. “In my region, we have almost 8 million people for 2 geneticists. That creates a huge bottleneck in getting patients to a proper diagnosis.” Face2Gene, however, may assist in supporting the diagnostic process. “The tool can aid primary care doctors in identifying cases that might benefit from genetic evaluation and allows specialists to prioritize testing more effectively.”

Dr. Bordest emphasizes that Face2Gene is not just for geneticists. “Pediatricians can use it as a support tool to differentiate between normal variations in appearance and potential genetic syndromes.” He shared a powerful example: “One of my former residents saw a child with short stature and used Face2Gene. The app flagged features consistent with Noonan Syndrome, supporting her clinical decision to order an echocardiogram. The heart scan revealed an undiagnosed congenital heart defect, allowing for early intervention before the child even reached my office.”

Teaching the Next Generation

As a preceptor at a major hospital, Dr. Bordest ensures that all his students, from general medical trainees to pediatric residents, are exposed to Face2Gene. “Every week, we use the app in the clinic, and I encourage my students to install it and test cases themselves. They start to see patterns and gain familiarity with how the tool may assist in clinical reasoning.”

The response from his students has been overwhelmingly positive. “Many of them now use Face2Gene regularly. Some have even contributed to important case insights during their shifts. I tell them, ‘If you ever feel uncertain about a case, use the app. If you don’t see anything unusual, use it anyway.”

Dr. Bordest believes Face2Gene should be more widely promoted among pediatricians. “At genetic conferences, everyone already knows about it. But at pediatric conferences, very few doctors are aware of it. That’s where the real change needs to happen.” He sees a major opportunity to integrate Face2Gene into pediatric education and practice, helping reduce the time it takes for patients to receive an accurate diagnosis.

His experience has changed his own approach to using the app. “There was a time when I only used Face2Gene for very obvious cases, but now I use it for everything. Even when I don’t suspect a syndrome, I take a photo and check. The technology may help identify subtle features that are easy to overlook, even for experienced geneticists.”

Through his teaching and advocacy, Dr. Bordest is helping to ensure that more doctors have access to this powerful tool, ultimately improving the lives of patients with rare genetic conditions.

The post The Importance of Teaching Face2Gene to Pediatricians appeared first on FDNA™.

The Early Days of Face2Gene

Face2Gene was born from a vision to leverage AI in diagnosing genetic syndromes through facial analysis. The inspiration initially stemmed from conditions such as Down syndrome and Noonan syndrome, where distinct facial features make identification more straightforward. The challenge – and ultimate goal – was to extend this capability to a wider range of syndromes using deep learning, enabling more precise and efficient diagnoses

“In the early days, our team drew inspiration from facial recognition advancements in AI,” Aviram explains. “Between 2015 and 2017, facial recognition technology surpassed human capabilities, with models like DeepFace and FaceNet leading the field. We wanted to pivot this technology from identifying individuals to recognizing facial patterns associated with genetic syndromes.”

Building the AI Model

The development process involved several stages. Initially, researchers collected data and experimented with classical machine learning models, such as support vector machines (SVMs). However, these early models lacked the sophistication required for accurate syndrome classification. We clearly needed to reach a richer phenotypic representation of the facial images.

“We started with many different models, each attempting to classify a syndrome from an image,” Aviram recalls. “But soon, we realized that training a single, unified model to recognize multiple syndromes at once, produced better results.”

A breakthrough occurred when the team harnessed large-scale datasets to train deep learning models. By analyzing thousands of images, the AI system learned to extract relevant facial features and classify genetic syndromes with greater accuracy. This revolutionary technology became known as DeepGestalt.

“DeepGestalt transforms facial images into numerical vectors, which represent key facial features,” Aviram explains. “These vectors are then used to predict syndromes with increasing accuracy as more data is incorporated into the training process.”

The Role of Data in AI Training

One of the biggest challenges in developing Face2Gene was obtaining high-quality, diverse training data. Genetic syndromes vary widely, and many are rare, making it difficult to collect enough images for robust model training.

“We needed many thousands of images to train the model effectively,” Aviram explains. “At first, we relied on public databases along with published research papers. We also partnered with clinicians and geneticists to expand our dataset, and had to design ways in which we could use the images while these were fully de-identified.”

As more clinicians adopted Face2Gene, the system continuously improved through machine learning. Each new diagnosis provided valuable feedback, refining the algorithm model’s accuracy over time.

Overcoming Limitations and Expanding to New Syndromes

Despite its success and good performance, Face2Gene’s technology faced inherent limitations, particularly when it comes to ultra-rare disorders. To address this, FDNA developed patient-matching initiatives, connecting facial images of individuals with similar phenotypic traits to enhance the understanding of rare conditions. “Most genetic syndromes fall into what we call the ‘long tail’ – conditions with very few documented cases,” Aviram explains.” To overcome this, we developed a patient-matching approach that groups similar cases, enhancing our ability to identify rare syndromes. We called this technology GestaltMatcher.”

The Evolution from DeepGestalt to GestaltMatcher

Aviram explains that the DeepGestalt technology employs deep learning to analyze facial phenotypes and classify them into predefined syndromes. While highly effective, it has an inherent limitation: it can only identify conditions already present in its database. If a syndrome is absent from the system, DeepGestalt cannot recognize or categorize it.

GestaltMatcher addresses this constraint by introducing an innovative approach. Rather than relying solely on classification, it converts facial images into numerical vectors that represent distinct phenotypic features. “These vectors are then compared to detect similarities between patients, even if their syndrome is not yet classified in the database. This method enables the delineation of new syndromes by clustering patients with shared phenotypic characteristics.”

Aviram continues by saying that GestaltMatcher has the potential to revolutionize genetic diagnostics. “Its ability to analyze and compare phenotypic data in a mathematical space opens the door to improved precision in diagnosing rare genetic disorders, enhanced research opportunities for uncovering new gene-to-syndrome correlations and facilitates collaborations between geneticists and companies to develop targeted therapies for undiagnosed patients.”

By continuously refining the technology and expanding its dataset, FDNA aims to further improve GestaltMatcher’s accuracy and usability in both clinical and research settings

How GestaltMatcher Works

The process begins with an image of a patient’s face, which is passed through multiple layers of a neural network. Each layer applies a series of filters, progressively extracting and refining relevant features. The final output is a numerical vector that captures the unique phenotypic characteristics of the patient.

Unlike DeepGestalt, which classifies images based on a predefined list of syndromes, GestaltMatcher measures the similarity between these vectors. By calculating the distance between vectors, the system can group patients with similar facial phenotypes, even when their condition has never been previously documented.

This newer approach allows researchers to:

• Identify undiagnosed patients with similar phenotypic features.
• Cluster patients based on facial similarities rather than a pre-existing diagnosis.
• Support the delineation and description of new genetic syndromes and possibly molecular pathways, through phenotype-based clustering.

“The development of the GestaltMatcher algorithm has only been possible using DeepGestalt as a basis. We demonstrated in the 2022 Nature Genetics publication that without its rich phenotypic representation, the GestaltMatcher results would be far less accurate,” highlights Aviram

The Future of AI in Genetic Diagnosis

Face2Gene has already transformed genetic diagnostics, yet its potential remains far from fully realized. As AI models become more sophisticated and datasets expand, both the accuracy and scope of genetic diagnosis will reach unprecedented levels.

“We are only beginning to unlock AI’s true potential in genetics,” Aviram concludes. “By continuously refining our algorithmic models and integrating diverse sources for phenotypic descriptions, from medical notes using Large Language Models, to various imaging modalities beyond facial analysis, and fostering global collaborations within the genetics community, we aim to accelerate rare disease diagnoses, enhance precision, and make genetic insights universally accessible.”

Face2Gene stands as a testament to the transformative power of AI in medicine, offering hope to families and clinicians searching for answers in the complex world of genetic disorders.

The post The Evolution of FDNA’s technology: An Interview with Aviram Bar Haim appeared first on FDNA™.

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The post Webinar: Matching ultra-rare cases in the clinic appeared first on FDNA™.

But what many may not realize is that AI is also helping to solve some of the world’s biggest problems in healthcare. Facebook is now using a mental health tracker that utilizes an algorithm that detects users who may be at risk of committing suicide. The tool analyzes user posts and comments that may indicate that a person is in crisis. Chatbots are being used in another AI-based technology to alert those who may have symptoms that resemble the Zika virus.

Solving Medical Mysteries via Facial Analysis

AI-based technology is currently making its mark on the healthcare industry through the identification of rare genetic syndromes. People born with these syndromes often have specific facial features that serve as indicators of the existence of a potential problem. However, the work involved in properly identifying the syndrome through a physician and a team of lab assistants alone can take several years. The challenge facing clinical healthcare staff and their patients is that there are thousands of possible syndromes, making it nearly impossible to make a positive diagnosis in a shorter amount of time. This is where AI-based technology comes in—to support clinicians in reaching a proper diagnosis by paring down the number of potential syndromes to only a handful of possibilities.

Currently, there is one AI-based method that uses facial analysis to identify rare genetic syndromes based on the physical manifestation of these syndromes. This process is called next-generation phenotyping (NGP).

While advancements in technology are constantly being made, it’s crucial to remember that AI isn’t perfect. Rather, the technology is used to support clinicians in making a diagnosis and not remove them completely from the process.

The Hype vs. Hope

Different from facial analysis, but often mistaken, facial recognition has received mixed reviews, but more often than not people are supportive of the innovation and advancements it affords. It can be used in a number of settings including banks to validate identity, tracking retail customers for marketing purposes, and tracking drivers for alertness while on the road. The loudest voices who are against the use of AI in facial recognition are those who say the technology invades personal privacy and increases the possibility of misuse of information.

At the present moment, there are little to no rules governing the use of AI, but we’re seeing a steady increase in the level of legislation coming out to protect both the consumer and developer. As this continues, the hope is that these laws won’t cripple the use of AI, but rather benefit those who desire to use it for good.

Final Thoughts

As an advocate for tech innovation and the responsible use of AI, I think it’s important to drive home the point that AI is only as good as the data it’s trained on. AI companies in healthcare are committed to protecting privacy by omitting any personal identifiers to the data used to train the neural networks, and rely solely on a trained healthcare provider to use and interpret the information. While the task at hand is gargantuan, I believe that this is an exciting time for tech-driven change for better healthcare.

The post AI-Driven Facial Analysis Equals Better Healthcare for All 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™.