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control population and population with Williams-Beuren syndrome.

Machine learning tool detects the risk of genetic syndromes

control population and population with Williams-Beuren syndrome.

(A) Control population. (B) Population with Williams-Beuren syndrome. Average faces were generated for each demographic group after automatic face pose correction.

With an average accuracy of 88%, a deep learning technology offers rapid genetic screening that could accelerate the diagnosis of genetic syndromes, recommending further investigation or referral to a specialist in seconds, according to a study published in The Lancet Digital Health. Trained with data from 2,800 pediatric patients from 28 countries, the technology also considers the face variability related to sex, age, racial and ethnic background, according to the study led by Children’s National Hospital researchers.

“We built a software device to increase access to care and a machine learning technology to identify the disease patterns not immediately obvious to the human eye or intuition, and to help physicians non-specialized in genetics,” said Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital and senior author of the study. “This technological innovation can help children without access to specialized clinics, which are unavailable in most of the world. Ultimately, it can help reduce health inequality in under-resourced societies.”

This machine learning technology indicates the presence of a genetic syndrome from a facial photograph captured at the point-of-care, such as in pediatrician offices, maternity wards and general practitioner clinics.

“Unlike other technologies, the strength of this program is distinguishing ‘normal’ from ‘not-normal,’ which makes it an effective screening tool in the hands of community caregivers,” said Marshall L. Summar, M.D., director of the Rare Disease Institute at Children’s National. “This can substantially accelerate the time to diagnosis by providing a robust indicator for patients that need further workup. This first step is often the greatest barrier to moving towards a diagnosis. Once a patient is in the workup system, then the likelihood of diagnosis (by many means) is significantly increased.”

Every year, millions of children are born with genetic disorders — including Down syndrome, a condition in which a child is born with an extra copy of their 21st chromosome causing developmental delays and disabilities, Williams-Beuren syndrome, a rare multisystem condition caused by a submicroscopic deletion from a region of chromosome 7, and Noonan syndrome, a genetic disorder caused by a faulty gene that prevents normal development in various parts of the body.

Most children with genetic syndromes live in regions with limited resources and access to genetic services. The genetic screening may come with a hefty price tag. There are also insufficient specialists to help identify genetic syndromes early in life when preventive care can save lives, especially in areas of low income, limited resources and isolated communities.

“The presented technology can assist pediatricians, neonatologists and family physicians in the routine or remote evaluation of pediatric patients, especially in areas with limited access to specialized care,” said Porras et al. “Our technology may be a step forward for the democratization of health resources for genetic screening.”

The researchers trained the technology using 2,800 retrospective facial photographs of children, with or without a genetic syndrome, from 28 countries, such as Argentina, Australia, Brazil, China, France, Morocco, Nigeria, Paraguay, Thailand and the U.S. The deep learning architecture was designed to account for the normal variations in the face appearance among populations from diverse demographic groups.

“Facial appearance is influenced by the race and ethnicity of the patients. The large variety of conditions and the diversity of populations are impacting the early identification of these conditions due to the lack of data that can serve as a point of reference,” said Linguraru. “Racial and ethnic disparities still exist in genetic syndrome survival even in some of the most common and best-studied conditions.”

Like all machine learning tools, they are trained with the available dataset. The researchers expect that as more data from underrepresented groups becomes available, they will adapt the model to localize phenotypical variations within more specific demographic groups.

In addition to being an accessible tool that could be used in telehealth services to assess genetic risk, there are other potentials for this technology.

“I am also excited about the potential of the technology in newborn screening,” said Linguraru. “There are approximately 140 million newborns every year worldwide of which eight million are born with a serious birth defect of genetic or partially genetic origin, many of which are discovered late.”

Children’s National as well recently announced that it has entered into a licensing agreement with MGeneRx Inc. for its patented pediatric medical device technology. MGeneRx is a spinoff from BreakThrough BioAssets LLC, a life sciences technology operating company focused on accelerating and commercializing new innovations, such as this technology, with an emphasis on positive social impact.

“The social impact of this technology cannot be underestimated,” said Nasser Hassan, acting chief executive officer of MGeneRx Inc. “We are excited about this licensing agreement with Children’s National Hospital and the opportunity to enhance this technology and expand its application to populations where precision medicine and the earliest possible interventions are sorely needed in order to save and improve children’s lives.”

little girl measuring her height

Study may change treatment of childhood growth disorders

little girl measuring her height

A new Phase 2 study at Children’s National will look at using the drug vosoritide to promote growth in children with growth disorders.

A child’s growth is often measured by pediatricians during routine physicals to identify abnormalities of growth and stature. An abnormality in these measurements could mean a child has a growth disorder. There are many different causes of growth disorders. Some can be the result of defects in genes related to the growth plate, which is the tissue near the end of long bones that grows as the child grows. Children with a growth disorder can present many different symptoms including short stature, joint pain, heart problems, bone problems and developmental delays. Scientists still have a lot to learn about what exactly causes these genetic growth disorders and treatments are limited, especially in the pediatric population. Growth hormone is not uniformly helpful and has only been approved for a small number of conditions.

Vosoritide is an investigational drug that directly targets the growth plate to promote growth. It is an analog of the amino acid C-type natriuretic peptide (CNP). It binds its receptor on healthy cartilage cells called chondrocytes and is currently under investigation in clinical trials as a treatment for the bone growth disorder achondroplasia.

“Vosoritide for Selected Genetic Causes of Short Stature” is a Phase 2 study currently open at Children’s National Hospital. This study will target five types of genetic short stature including SHOX deficiency, hypochondroplasia, Rasopathies (which includes Noonan syndrome), heterozygous NPR2 defects and CNP deficiency.

Thirty-five children with short stature will be enrolled and followed for a six-month observation period to obtain baseline growth velocity, safety profile and quality of life assessment. Study participants will then be treated with vosoritide for 12 months and will be assessed for safety and improvement in growth outcomes.

“Many patients who present with short stature likely have genetic defects in genes involved in growth plate physiology. Those patients with selected causes of short stature that interact with the CNP pathway may benefit from treatment with vosoritide, which directly targets the growth plate,” said Andrew Dauber, M.D., MMSc., lead investigator of this clinical study and chief of Endocrinology at Children’s National Hospital, a program ranked in the top 10 by U.S. News & World Report. “In this study, our goal is to understand if vosoritide may be a safe and effective treatment option for certain genetically defined short stature syndromes.”

This clinical trial has been approved by the FDA and funded by BioMarin. Children’s National is the only site in the world offering this therapy for patients with these conditions. The study is currently underway and subject recruitment is ongoing. There are 9 participants enrolled to date.

“This study could fundamentally change the way we treat certain growth disorders”, says Dr. Dauber.

For more information on the clinical trial, click here.

Children’s National leaders join with Governor Martin O'Malley

Facial analysis technology successfully used to identify Noonan syndrome in diverse populations

facial recognition of noonan syndrome

According to an international study led by the National Human Genome Research Institute (NHGRI), researchers have successfully used facial analysis software, developed by the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National, to identify Noonan syndrome in diverse populations.

Noonan syndrome is relatively common, affecting between 1 in 1,000 to 1 in 2,500 children, however few studies have been conducted in non-Europeans. For this study, the researchers evaluated children (average age of eight) with Noonan syndrome from 20 countries. Using the facial analysis software and clinical criteria, the researchers compared 161 white, African, Asian and Latin American children with Noonan syndrome with 161 people of the same age and gender without the disease. Using the software to analyze facial features, they were able to correctly diagnose patients with the disease from each ethnic group with 94 percent or higher accuracy.

“Our algorithm found widely spaced eyes as a significant facial feature in all ethnic groups and also highlighted facial features that are relevant to diagnosing the syndrome in each group,” said

Marius George Linguraru, D.Phil., developer of the facial analysis technology and an investigator in the study from Children’s National.

Linguraru and his team are working to create a simple tool that will enable doctors in clinics without state-of-the-art genetic facilities to take photos of their patients on a smartphone and receive instant results.