Posts

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.”

Coronavirus and lungs with world map in the background

Top AI models unveiled in COVID-19 challenge to improve lung diagnostics

Coronavirus and lungs with world map in the background

The top 10 results have been unveiled in the first-of-its-kind COVID-19 Lung CT Lesion Segmentation Grand Challenge, a groundbreaking research competition focused on developing artificial intelligence (AI) models to help in the visualization and measurement of COVID specific lesions in the lungs of infected patients, potentially facilitating more timely and patient-specific medical interventions.

Attracting more than 1,000 global participants, the competition was presented by the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital in collaboration with leading AI technology company NVIDIA and the National Institutes of Health (NIH). The competition’s AI models utilized a multi-institutional, multi-national data set provided by public datasets from The Cancer Imaging Archive (National Cancer Institute), NIH and the University of Arkansas, that originated from patients of different ages, genders and with variable disease severity. NVIDIA provided GPUs to the top five winners as prizes, as well as supported the selection and judging process.

“Improving COVID-19 treatment starts with a clearer understanding of the patient’s disease state. However, a prior lack of global data collaboration limited clinicians in their ability to quickly and effectively understand disease severity across both adult and pediatric patients,” says Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator at the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National, who led the Grand Challenge initiative. “By harnessing the power of AI through quantitative imaging and machine learning, these discoveries are helping clinicians better understand COVID-19 disease severity and potentially stratify and triage into appropriate treatment protocols at different stages of the disease.”

The top 10 AI algorithms were identified from a highly competitive field of participants who tested the data in November and December 2020. The results were unveiled on Jan. 11, 2021, in a virtual symposium, hosted by Children’s National, that featured presentations from top teams, event organizers and clinicians.

Developers of the 10 top AI models from the COVID-19 Lung CT Lesion Segmentation Grand Challenge are:

  1. Shishuai Hu, et al. Northwestern Polytechnical University, China. “Semi-supervised Method for COVID-19 Lung CT Lesion Segmentation”
  2. Fabian Isensee, et al. German Cancer Research Center, Germany. “nnU-Net for Covid Segmentation”
  3. Claire Tang, Lynbrook High School, USA. “Automated Ensemble Modeling for COVID-19 CT Lesion Segmentation”
  4. Qinji Yu, et al. Shanghai JiaoTong University, China. “COVID-19-20 Lesion Segmentation Based on nnUNet”
  5. Andreas Husch, et al. University of Luxembourg, Luxembourg. “Leveraging State-of-the-Art Architectures by Enriching Training Information – a case study”
  6. Tong Zheng, et al. Nagoya University, Japan. “Fully-automated COVID-19-20 Segmentation”
  7. Vitali Liauchuk. United Institute of Informatics Problems (UIIP), Belarus. “Semi-3D CNN with ImageNet Pretrain for Segmentation of COVID Lesions on CT”
  8. Ziqi Zhou, et al. Shenzhen University, China. “Automated Chest CT Image Segmentation of COVID-19 with 3D Unet-based Framework”
  9. Jan Hendrik Moltz, et al. Fraunhofer Institute for Digital Medicine MEVIS, Germany. “Segmentation of COVID-19 Lung Lesions in CT Using nnU-Net”
  10. Bruno Oliveira, et al. 2Ai – Polytechnic Institute of Cávado and Ave, Portugal. “Automatic COVID-19 Detection and Segmentation from Lung Computed Tomography (CT) Images Using 3D Cascade U-net”

Linguraru added that, in addition to an award for the top five AI models, these winning algorithms are now available to partner with clinical institutions across the globe to further evaluate how these quantitative imaging and machine learning methods may potentially impact global public health.

“Quality annotations are a limiting factor in the development of useful AI models,” said Mona Flores, M.D., global head of Medical AI, NVIDIA. “Using the NVIDIA COVID lesion segmentation model available on our NGC software hub, we were able to quickly label the NIH dataset, allowing radiologists to do precise annotations in record time.”

“I applaud the computer science, data science and image processing global academic community for rapidly teaming up to combine multi-disciplinary expertise towards development of potential automated and multi-parametric tools to better study and address the myriad of unmet clinical needs created by the pandemic,” said Bradford Wood, M.D., director, NIH Center for Interventional Oncology and chief, Interventional Radiology Section, NIH Clinical Center. “Thank you to each team for locking arms towards a common cause that unites the scientific community in these challenging times.”

communication network concept image

Children’s National joins international AI COVID-19 initiative

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.

kidney ultrasound

Using computers to enhance hydronephrosis diagnosis

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.