Posts

control population and population with Williams-Beuren syndrome.

Machine learning tool detects the risk of genetic syndromes

/in , , , /by
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.”

facial recognition of noonan syndrome

Commercialization of novel facial analysis technology can improve diagnosis of rare disorders in pediatric patients

/in , , , /by
facial recognition of noonan syndrome

Children’s National Hospital has entered into a licensing agreement with MGeneRx Inc. for its patented pediatric medical device technology using objective digital biometric analysis software for the early and non-invasive screening of dysmorphic genetic diseases such as Noonan syndrome.

Children’s National Hospital has entered into a licensing agreement with life sciences technology company MGeneRx Inc. for its patented pediatric medical device technology using objective digital biometric analysis software for the early and non-invasive screening of dysmorphic genetic diseases. The technology, developed by a multidisciplinary Children’s National team led by Marius George Linguraru, D.Phil, M.A., M.Sc., of the Sheikh Zayed Institute for Pediatric Surgical Innovation and Marshall Summar, M.D., director of the Children’s National Rare Disease Institute (CNRDI), can provide a more advanced diagnostic tool for regions of the world with limited access to geneticists or genetic testing.

The application utilizes artificial intelligence (AI) and machine learning to analyze biometric data and identify facial markers that are indicative of genetic disorders. Physicians can capture biometric data points of a child’s face in real time within the platform, where it scans facial biometric features to determine the potential presence of a genetic disease, which can often be life-threatening without early intervention. Research studies conducted in conjunction with the National Human Genome Research Institute at the National Institutes of Health further enhanced the development of the application in recent years, showing the potential to detect, with a 90 percent accuracy, early diagnosis of 128 genetic diseases across pediatric subjects in 28 countries. These diseases include DiGeorge syndrome (22q11.2 deletion syndrome), Down syndrome, Noonan syndrome and Williams-Beuren syndrome.

“We are delighted to enter into this licensing agreement through Innovation Ventures, the commercialization arm of Children’s National Hospital, which seeks to move inventions and discoveries from Children’s National to the marketplace to benefit the health and well-being of children. Our mission is to add the ‘D’ in development to the ‘R’ in research to accelerate the commercialization of our intellectual property,” says Kolaleh Eskandanian, Ph.D., M.B.A., P.M.P., vice president and chief innovation officer at Children’s National and managing director of Innovation Ventures. “It is through partnerships with startups and the industry that we can achieve this goal and thus we highly value this new partnership with MGeneRx Inc. The acceleration and commercialization of this objective digital biometric analysis technology will not only help diagnose rare genetic disorders – it will also allow for earlier interventions that improve the quality of life for the children living with these conditions.”

Eskandanian adds that the social impact of this technology is especially profound in lower income nations around the world, where there is a high prevalence of rare genetic conditions but a severe lack in the specialty care required to diagnose and treat them. Additional data collected through the expanded use of the technology will help to further develop the application and expand its capabilities to identify and diagnose additional rare genetic conditions.

The licensing agreement was arranged by the Children’s National Office of Innovation Ventures, which is focused on the commercialization of impactful new pediatric medical device technologies and therapies to advance children’s health care. Created to catalyze the ongoing translational research of the Children’s National Research Institute (CNRI) as well as inventions by hospital’s clinicians, Innovation Ventures focuses on four core pillars to advance pediatric medical technologies including a Biodesign program, partnerships and alliances to augment internal capacity, seed funding to de-risk technologies and validate market and clinical relevance, and back-office operations to manage intellectual property and licensing activities. Since 2017, Children’s National intellectual property has served as the basis for over 15 licensing or option agreements with commercial partners.

Providing access to an array of experts and resources for pediatric innovators is one of the aims of the Children’s National Research & Innovation Campus, a first-of-its-kind focused on pediatric health care innovation, with the first phase currently open on the former Walter Reed Army Medical Center campus in Washington, D.C. With its proximity to federal research institutions and agencies, universities, academic research centers, as well as on-site incubator Johnson and Johnson Innovation – JLABS, the campus provides a rich ecosystem of public and private partners, which will help bolster pediatric innovation and commercialization.

communication network concept image

Children’s National joins international AI COVID-19 initiative

/in , , , , , , , /by
communication network concept image

Children’s National Hospital is the first pediatric partner to join an international initiative led by leading technology firm NVIDIA and Massachusetts General Brigham Hospital, focused on creating solutions through machine and deep learning to benefit COVID-19 healthcare outcomes.

Children’s National Hospital is the first pediatric partner to join an international initiative led by leading technology firm NVIDIA and Massachusetts General Brigham Hospital, focused on creating solutions through machine and deep learning to benefit COVID-19 healthcare outcomes. The initiative, known as EXAM (EMR CXR AI Model) is the largest and most diverse federated learning enterprise, comprised of 20 leading hospitals from around the globe.

Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator at the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital, noted that one of the core goals of the initiative is to create a platform which brings resources together, from a variety of leading institutions, to advance the care of COVID-19 patients across the board, including children.

“Children’s National Hospital is proud to be the first pediatric partner joining the world’s leading healthcare institutions in this collaboration to advance global health,” says Linguraru. “We are currently living in a time where rapid access to this kind of global data has never been more important — we need solutions that work fast and are effective. That is not possible without this degree of collaboration and we look forward to continuing this important work with our partners to address one of the most significant healthcare challenges in our lifetime.”

A recent systematic review and meta-analysis from Children’s National Hospital became another core contribution to understanding how children are impacted by COVID-19. Led by Linguraru and accepted to be published in Pediatric Pulmonology, it offers the first comprehensive summary of the findings of various studies published thus far that describe COVID-19 lung imaging data across the pediatric population.

The review examined articles based on chest CT imaging in 1,026 pediatric patients diagnosed with COVID-19, and concluded that chest CT manifestations in those patients could potentially be used to prompt intervention across the pediatric population.

Marius George Linguraru

“Children’s National Hospital is proud to be the first pediatric partner joining the world’s leading healthcare institutions in this collaboration to advance global health,” says Marius George Linguraru, D.Phil., M.A., M.Sc.

“Until this point, pediatric COVID-19 studies have largely been restricted to case reports and small case series, which have prevented the identification of any specific pediatric lung disease patterns in COVID-19 patients,” says Linguraru. “Not only did this review help identify the common patterns in the lungs of pediatric patients presenting COVID-19 symptoms, which are distinct from the signs of other viral respiratory infections in children, it also provided insight into the differences between children and adults with COVID-19.”

Earlier this month, NVIDIA announced the EXAM initiative had – in just 20 days – developed an artificial intelligence (AI) model to determine whether a patient demonstrating COVID-19 symptoms in an emergency room would require supplemental oxygen hours – even days – after the initial exam. This data ultimately aids physicians in determining the proper level of care for patients, including potential ICU placement.

The EXAM initiative achieved a machine learning model offering precise prediction for the level of oxygen incoming patients would require.

In addition to Children’s National Hospital, other participants included Mass Gen Brigham and its affiliated hospitals in Boston; NIHR Cambridge Biomedical Research Centre; The Self-Defense Forces Central Hospital in Tokyo; National Taiwan University MeDA Lab and MAHC and Taiwan National Health Insurance Administration; Tri-Service General Hospital in Taiwan; Kyungpook National University Hospital in South Korea; Faculty of Medicine, Chulalongkorn University in Thailand; Diagnosticos da America SA in Brazil; University of California, San Francisco; VA San Diego; University of Toronto; National Institutes of Health in Bethesda, Maryland; University of Wisconsin-Madison School of Medicine and Public Health; Memorial Sloan Kettering Cancer Center in New York; and Mount Sinai Health System in New York.

illustration of lungs surrounded by virus

COVID-19: First comprehensive review of pediatric lung imaging features

/in , , , , /by
illustration of lungs surrounded by virus

A systematic review and meta-analysis by Children’s National Hospital researchers, published in Pediatric Pulmonology, provides the first comprehensive review of the findings of published studies describing COVID-19 lung imaging data in children.

The number COVID-19 studies focused on children have been small and with limited data. This has prevented the identification of specific pediatric lung disease patterns in COVID-19. Although children make up around 9.5% of COVID-19 infections, less than 2% of the literature on the virus, its symptoms and effects, have focused on kids.

A systematic review and meta-analysis by Children’s National Hospital researchers, published in Pediatric Pulmonology, provides the first comprehensive review of the findings of published studies describing COVID-19 lung imaging data in children. The analysis concludes that chest CT manifestations in children with COVID‐19 could potentially prompt intervention in the pediatric population.

Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National, discusses the importance of this work.

Q: What findings stand out to you?

A: We found that more than a third of children with COVID-19 had normal imaging. The lung imaging findings in these children were overall less frequent and less severe than in adult patients, but they were also more heterogeneous than in adults. Importantly, children with COVID-19 were three times more likely to have a normal exam than adults.

Several common lung imaging findings reported in adults were extremely rare or not found in the pediatric studies. These discoveries, and other recent reports in this space, support the fact that children’s symptoms may be less obvious than adults or even absent, but they still carry the virus and may be at risk for serious and life-threatening illness.

Marius George Linguraru

Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National.

Q: How will the findings of this study benefit pediatric care?

A: In our study, we showed how the health of the lungs of these children is impacted. Our results from data from 1,026 children (from newborns to 18 year old) with COVID-19 present chest manifestations that could potentially prompt informed intervention and better recovery.

Another conclusion of our study is that the abnormalities reported on the chest scans of children infected with COVID-19 are distinct from the typical lung images seen during other viral respiratory infections in the pediatric population. This is important for preparing for the cold and flu season.

Q: Why was this review important to our understanding of how COVID-19 impacts children?

A: This is the first systematic review and meta-analysis focused on the manifestation of the COVID-19 infection in the lungs of children. Our study, and others from colleagues at Children’s National, helps lead the efforts on elucidating how the pandemic affects the health of children.

Though children were initially thought to be less susceptible to infection, the data has made it clear that many children are at high risk for hospitalization and severe health complications. Although there are similarities between how children and adults are affected by the pandemic, there are also critical differences.

Given the limited knowledge in the manifestation of COVID-19 in children, with children susceptible to infection and hospitalization, and with children returning to school, continued efforts to understand the impact of COVID-19 on young patients is critically important. Understanding how children fare through the pandemic is the foundation of discovering better ways to take care of young patients and their health.

You can find the full study published in Pediatric Pulmonology. Learn more about the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National.

$1M grant funds research on quantitative imaging for tumors

/in , , , /by

“For children who are at risk of losing their vision, this project will bring a window of opportunity for physicians to start treatment earlier and save their vision,” says Marius George Linguraru, DPhil, MA, MSc.

A team from Children’s National Hospital is part of a project receiving a two-year grant of nearly $1,000,000 from the National Institutes of Health (NIH) for the first pediatric project in the Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI). Marius George Linguraru, DPhil, MA, MSc, principal investigator from the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital in Washington, D.C., is one of two principal investigators on the project, which focuses on developing quantitative imaging (QI) tools to improve pediatric tumor measurement, risk predictions and treatment response. Roger Packer, M.D., Senior Vice President of the Center for Neuroscience & Behavioral Health, Director of the Gilbert Neurofibromatosis Institute and Director of the Brain Tumor Institute, is co-investigator.

The project, in collaboration with Children’s Hospital of Philadelphia and Children’s Hospital Colorado, centers on the most common type of brain tumor in children, called a low-grade glioma. This project focuses on a clinically challenging group of children with neurofibromatosis type 1 (NF1), the most common inherited tumor predisposition syndrome. Nearly 20% of children with NF1 will develop a low-grade glioma called optic pathway glioma (OPG). In children with this type of brain tumor, the growth occurs around the optic nerve, chiasm and tracts, also called the optic pathway, which connects the eye to the brain. OPGs can cause vision loss and even blindness. Permanent vision loss usually occurs between one and eight years of age with doctors closely monitoring the tumor with magnetic resonance imaging (MRI) to assess the disease progression.

“Our traditional two-dimensional measures of tumor size are not appropriate to assess the changes in these amorphous tumors over time or how the tumor responds to treatment,” says Linguraru. “This means physicians have difficulty determining the size of the tumor as well as when treatment is working. Research such as this can lead to innovative medical technologies that can improve and possibly change the fate of children’s lives.”

Dr. Linguraru is leading the technical trials on this project, which take place in the first two years, or phase one, starting in June 2020. Phase one focuses on improving the often inaccurate human measurements of tumor size by developing QI tools to make precise and automated measures of tumor volume and shape using machine learning. In this phase, the project will use and homogenize MRI data from multiple centers to develop predictive models of the treatment response based on the tumor volume that are agnostic to the differences in imaging protocols. By doing this, it will allow physicians to make more informed decisions about the treatment’s success and whether the child will recover their vision.

When phase one is complete, Linguraru and the project’s other principal investigator Robert A. Avery, DO, MSCE, neuro-ophthalmologist in the Division of Ophthalmology at Children’s Hospital of Philadelphia, will initiate the second phase, which includes validating the QI application on data from the first ever phase III clinical trial comparing two treatments for NF1-OPGs. Phase two is scheduled to start in the Summer 2022 and continue through Summer 2025.

“For children who are at risk of losing their vision, this project will bring a window of opportunity for physicians to start treatment earlier and save their vision,” says Linguraru. “For those children who won’t benefit from chemotherapy because the tumor poses no threat to their sight, this project will save them from having to go through that difficult treatment unnecessarily. It will be life-changing for the children and their families, which is what excites me about this QI application.”

This project is a collaboration between Children’s Hospital of Philadelphia and Children’s National Hospital in Washington, D.C., in partnership with Children’s Hospital of Colorado and University of Pennsylvania. Upon project completion, the QI application will provide a precision-medicine approach for NF1-OPGs and improve clinical outcomes for pediatric tumors.

kidney ultrasound

Using computers to enhance hydronephrosis diagnosis

/in , , , /by
kidney ultrasound

Researchers at Children’s National Hospital are using quantitative imaging and machine intelligence to enhance care for children with a common kidney disease, and their initial results are very promising. Their technique provides an accurate way to predict earlier which children with hydronephrosis will need surgical intervention, simplifying and enhancing their care.

We live in a time of great uncertainty yet great promise, particularly when it comes to harnessing technology to improve lives. Researchers at Children’s National Hospital are using quantitative imaging and machine intelligence to enhance care for children with a common kidney disease, and their initial results are very promising. Their technique provides an accurate way to predict earlier which children with hydronephrosis will need surgical intervention, simplifying and enhancing their care.

Hydronephrosis means “water in the kidney” and is a condition in which a kidney doesn’t empty normally. One of the most frequently detected abnormalities on prenatal ultrasound, hydronephrosis affects up to 4.5% of all pregnancies and is often discovered prenatally or just after birth.

Although hydronephrosis in children sometimes resolves by itself, identifying which kidneys are obstructed and more likely to need intervention isn’t particularly easy. But it is critical. “Children with severe hydronephrosis over long periods of time can start losing kidney function to the point of losing a kidney,” says Marius George Linguraru, DPhil, MA, MSc, principal investigator of the project; director of Precision Medical Imaging Group at the Sheikh Zayed Institute for Pediatric Surgical Innovation; and professor of radiology, pediatrics and biomedical engineering at George Washington University.

Children with hydronephrosis face three levels of examination and intervention: ultrasound, nuclear imaging testing called diuresis renogram and surgery for the critical cases. “What we want to do with this project is stratify kids as early as possible,” Dr. Linguraru says. “The earlier we can predict, the better we can plan the clinical care for these kids.”

Ultrasound is used to see whether there is a blockage and try to determine hydronephrosis severity. “Ultrasound is non-invasive, non-radiating, and does not expose the child to any risk prenatally or postnatally,” Dr. Linguraru says. Ultrasound evaluations require a trained radiologist, but there’s a lot of variability. Radiologists have a grading system based on the ultrasound appearance of the kidney to determine whether the hydronephrosis is mild, moderate or severe, but studies show this isn’t predictive of longer term outcomes.

Children whose ultrasounds show concern will be referred to diuresis renogram. Costly, complex, invasive and irradiating, it tests how well the kidney empties. Although appropriate for good clinical indications, doctors try to minimize its use. “Management of hydronephrosis is complex,” Dr. Linguraru says. “We want to use ultrasound as much as possible and much less diuresis renogram.”

For those patients whose kidney is obstructed and eventually need surgical intervention, the sooner that decision can be made the better. “The more you wait for a kidney that is severely obstructed, the more function may be lost. If intervention is required, it’s preferable to do it early,” Dr. Linguraru says. Of course for the child whose hydronephrosis will likely resolve itself, intervention is not the best option.

Marius George Linguraru

“With our technique we are measuring physiological and anatomical changes in the ultrasound image of the kidney,” says Marius George Linguraru, DPhil, MA, MSc. “The human eye may find it difficult to put all this together, but the machine can do it. We use quantitative imaging to do deep phenotyping of the kidney and machine learning to interpret the data.”

Dr. Linguraru and the multidisciplinary team at Children’s National Hospital, including radiology and urology clinicians, are putting the power of computers to work interpreting subtleties in the ultrasound data that humans just can’t see. In their pilot study they found that 60% of the nuclear imaging tests could have been safely avoided without missing any of the critical cases of hydronephrosis. “With our technique we are measuring physiological and anatomical changes in the ultrasound image of the kidney,” Dr. Linguraru says. “The human eye may find it difficult to put all this together, but the machine can do it. We use quantitative imaging to do deep phenotyping of the kidney and machine learning to interpret the data.”

Results of the initial study indicate that kids who have a mild condition can be safely discharged earlier and the model can predict all those kids with obstructions and accelerate their diagnosis by sending them earlier to get further investigation. Dr. Linguraru says. “There are only benefits: some kids will get earlier diagnosis, some earlier discharges.”

The team also has a way to improve the interpretation of diuresis renograms. “We analyze the dynamics of the kidney’s drainage curve in quantifiable way. Using machine learning to interpret those results, we showed we can potentially discharge some kids earlier and accelerate intervention for the most severe cases instead of waiting and repeating the invasive tests,” he says. The framework has 93% accuracy, including 91% sensitivity and 96% specificity, to predict surgical cases, a significant improvement over clinical metrics’ accuracy.

The next step is a study connecting all the protocols. “Right now we have a study on ultrasound, a study on nuclear imaging, but we need to connect them so a child with hydronephrosis immediately benefits,” says Dr. Linguraru. Future work will focus on streamlining and accelerating diagnosis and intervention for kids who need it, both in prospective studies and hopefully clinically as well.

Hydronephrosis is an area in which machine learning can be applied to pediatric health in meaningful ways because of the sheer volume of cases.

“Machine learning algorithms work best when they are trained well on a lot of data,” Dr. Linguraru says. “Often in pediatric conditions, data are sparse because conditions are rare. Hydronephrosis is one of those areas that can really benefit from this new technological development because there is a big volume of patients. We are collecting more data, and we’re becoming smarter with these kinds of algorithms.”

Learn more about the Precision Medical Imaging Laboratory and its work to enhance clinical information in medical images to improve children’s health.