“The future is in AI and machine learning and how it allows large data sets to be utilized to a level of understanding that we currently don’t have…We have very rare monogenetic disorders where single gene is the cause of certain inflammatory valve diseases in young children and we’re starting to learn about what’s the right therapy by that gene and personalizing medicine… Not just precision medicine (which is better for a population) but really personalizing medicine.”
Learn more about what Ian Leibowitz, M.D., division chief of Gastroenterology, Hepatology and Nutrition Services, says as he discusses advances in clinical care algorithms that facilitate the timely diagnosis of critical conditions, efforts to increase access to medical and surgical treatment, and broaden awareness among primary care physicians to help ensure care is available and provided as early as possible to all patients.
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Artificial intelligence (AI) has the potential to detect rheumatic heart disease (RHD) with the same accuracy as a cardiologist, according to new research demonstrating how sophisticated deep learning technology can be applied to this disease of inequity. The work could prevent hundreds of thousands of unnecessary deaths around the world annually.
Developed at Children’s National Hospital and detailed in the latest edition of the Journal of the American Heart Association, the new AI system combines the power of novel ultrasound probes with portable electronic devices installed with algorithms capable of diagnosing RHD on echocardiogram. Distributing these devices could allow healthcare workers, without specialized medical degrees, to carry technology that could detect RHD in regions where it remains endemic.
RHD is caused by the body’s reaction to repeated Strep A bacterial infections and can cause permanent heart damage. If detected early, the condition is treatable with penicillin, a widely available antibiotic. In the United States and other high-income nations, RHD has been almost entirely eradicated. However, in low- and middle-income countries, it impacts the lives of 40 million people, causing nearly 400,000 deaths a year.
“This technology has the potential to extend the reach of a cardiologist to anywhere in the world,” said Kelsey Brown, M.D., a cardiology fellow at Children’s National and co-lead author on the manuscript with Staff Scientist Pooneh Roshanitabrizi, Ph.D. “In one minute, anyone trained to use our system can screen a child to find out if their heart is demonstrating signs of RHD. This will lead them to more specialized care and a simple antibiotic to prevent this degenerative disease from critically damaging their hearts.”
The big picture
The new AI system combines the power of novel ultrasound probes with portable electronic devices installed with algorithms capable of diagnosing RHD on echocardiogram.
Millions of citizens in impoverished countries have limited access to specialized care. Yet the gold standard for diagnosing RHD requires a highly trained cardiologist to read an echocardiogram — a non-invasive and widely distributed ultrasound imaging technology. Without access to a cardiologist, the condition may remain undetected and lead to complications, including advanced cardiac disease and even death.
According to the new research, the AI algorithm developed at Children’s National identified mitral regurgitation in up to 90% of children with RHD. This tell-tale sign of the disease causes the mitral valve flaps to close improperly, leading to backward blood flow in the heart.
Beginning in March, Craig Sable, M.D., interim division chief of Cardiology, and his partners on the project will implement a pilot program in Uganda incorporating AI into the echo screening process of children being checked for RHD. The team believes that a handheld ultrasound probe, a tablet and a laptop — installed with the sophisticated, new algorithm — could make all the difference in diagnosing these children early enough to change outcomes.
“One of the most effective ways to prevent rheumatic heart disease is to find the patients that are affected in the very early stages, give them monthly penicillin for pennies a day and prevent them from becoming one of the 400,000 people a year who die from this disease,” Dr. Sable said. “Once this technology is built and distributed at a scale to address the need, we are optimistic that it holds great promise to bring highly accurate care to economically disadvantaged countries and help eradicate RHD around the world.”
Children’s National Hospital leads the way
To devise the best approach, two Children’s National experts in AI — Dr. Roshanitabrizi and Marius George Linguraru, D.Phil., M.A., M.Sc., the Connor Family Professor in Research and Innovation and principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation — tested a variety of modalities in machine learning, which mimics human intelligence, and deep learning, which goes beyond the human capacity to learn. They combined the power of both approaches to optimize the novel algorithm, which is trained to interpret ultrasound images of the heart to detect RHD.
Already, the AI algorithm has analyzed 39 features of hearts with RHD that cardiologists cannot detect or measure with the naked eye. For example, cardiologists know that the heart’s size matters when diagnosing RHD. Current guidelines lay out diagnostic criteria using two weight categories — above or below 66 pounds — as a surrogate measure for the heart’s size. Yet the size of a child’s heart can vary widely in those two groupings.
“Our algorithm can see and make adjustments for the heart’s size as a continuously fluid variable,” Dr. Roshanitabrizi said. “In the hands of healthcare workers, we expect the technology to amplify human capabilities to make calculations far more quickly and precisely than the human eye and brain, saving countless lives.”
Among other challenges, the team had to design new ways to teach the AI to handle the inherent clinical differences found in ultrasound images, along with the complexities of evaluating color Doppler echocardiograms, which historically have required specialized human skill to evaluate.
“There is a true art to interpreting this kind of information, but we now know how to teach a machine to learn faster and possibly better than the human eye and brain,” Dr. Linguraru said. “Although we have been using this diagnostic and treatment approach since World War II, we haven’t been able to share this competency globally with low- and middle-income countries, where there are far fewer cardiologists. With the power of AI, we expect that we can, which will improve equity in medicine around the world.”
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Advanced MRI visualization techniques to follow blood flow in the hearts of cardiac patients. Gene therapy for pediatric patients with Duchenne muscular dystrophy. 3D-printed casts for treating clubfoot. These were among the most popular articles we published on Innovation District in 2023. Read on for our full list.
Cardiac imaging specialists and cardiac surgeons at Children’s National Hospital are applying advanced magnetic resonance imaging visualization techniques to understand the intricacies of blood flow within the heart chambers of children with single ventricle heart defects like hypoplastic left heart syndrome. The data allows surgeons to make critical corrections to the atrioventricular valve before a child undergoes the single ventricle procedure known as the Fontan. (3 min. read)
Children’s National Hospital became the first pediatric hospital to administer a commercial dose of Elevidys (delandistrogene moxeparvovec-rokl), the first gene therapy for the treatment of pediatric patients with Duchenne muscular dystrophy (DMD). Elevidys is a one-time intravenous gene therapy that aims to delay or halt the progression of DMD by delivering a modified, functional version of dystrophin to muscle cells. (2 min. read)
Researchers at Children’s National Hospital developed a pre-clinical model to test drugs and therapies for Becker Muscular Dystrophy (BMD), a debilitating neuromuscular disease that is growing in numbers and lacks treatment options. The work provides scientists with a much-needed method to identify, develop and de-risk drugs for patients with BMD. (2 min. read)
In 2022, five newborns with life-threatening congenital heart disease affecting their heart rhythms were the first in the United States to receive a novel modified pacemaker generator to stabilize their heart rhythms within days of birth. Two of the five cases were cared for at Children’s National Hospital. In a follow-up article, the team at Children’s National shared that “early post-operative performance of this device has been excellent.” (2 min. read)
Experts from the Food and Drug Administration, Pfizer, Oracle Health, NVIDIA, AWS Health and elsewhere came together to discuss how pediatric specialties can use AI to provide medical care to kids more efficiently, more quickly and more effectively at the inaugural symposium on AI in Pediatric Health and Rare Diseases, hosted by Children’s National Hospital and the Fralin Biomedical Research Institute at Virginia Tech. (3 min. read)
The American Academy of Pediatrics (AAP) named a 2019 study led by clinician-researchers at Children’s National Hospital one of the 12 most influential Pediatric Emergency Medicine articles ever published in the journal Pediatrics. The findings showed that states with stricter gun laws and laws requiring universal background checks for gun purchases had lower firearm-related pediatric mortality rates but that more investigation was needed to better understand the impact of firearm legislation on pediatric mortality. (2 min. read)
Children’s National Hospital recently welcomed pediatric and adult colorectal surgeon Erin Teeple, M.D., to the Division of Colorectal and Pelvic Reconstruction. Dr. Teeple is the only person in the United States who is board-certified as both a pediatric surgeon and adult colorectal surgeon, uniquely positioning her to care for people with both acquired and congenital colorectal disease and help them transition from pediatric care to adult caregivers. (3 min. read)
The sickle cell team at Children’s National Hospital received a grant from the Founders Auxiliary Board to launch a first-of-its-kind, personalized holistic transformative program for the management of pain in sickle cell disease. The clinic uses an inter-disciplinary approach of hematology, psychology, psychiatry, anesthesiology/pain medicine, acupuncture, mindfulness, relaxation and aromatherapy services. (3 min read)
Non-invasive prenatal testing using cell-free DNA (cfDNA) is currently offered to all pregnant women regardless of the fetal risk. In a study published in the American Journal of Obstetrics and Gynecology, researchers from Children’s National Hospital provided context and expert recommendations for maternal and fetal evaluation and management when cfDNA screening is positive for monosomy X or Turner Syndrome. (2 min. read)
While clubfoot is relatively common and the treatment is highly successful, the weekly visits required for Ponseti casting can be a significant burden on families. Researchers at Children’s National Hospital are looking for a way to relieve that burden with a new study that could eliminate the weekly visits with a series of 3D-printed casts that families can switch out at home. (1 min. read)
A new validated self-report tool provides researchers with a way to characterize the gender of research participants beyond their binary designated sex at birth. The multi-dimensional Gender Self-Report, developed using a community-driven approach and then scientifically validated, was outlined in a peer-reviewed article in the American Psychologist, a journal of the American Psychological Association. (2 min. read)
In a study published by Advances in Chronic Kidney Disease, a team at Children’s National Hospital reviewed cardiovascular and bone diseases in chronic kidney disease and end-stage kidney disease patients with a focus on pediatric issues and concerns. (1 min. read)
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Federated learning can solve data-sharing challenges, allowing nimble collaboration across institutions to drive medical advances using artificial intelligence (AI).
Federated learning can solve data-sharing challenges, allowing nimble collaboration across institutions to drive medical advances using artificial intelligence (AI), according to a new manuscript from 10 thought leaders in AI and machine learning in medicine.
In Health Informatics Journal, these leading experts on how technology is shaping medicine shared a conversation that they had at the Radiology Society of North America’s conference. They weighed challenges facing AI, including barriers to data sharing because of privacy rules that prevent the distribution of information to different institutions. With federated learning, models are shared – rather than data – allowing institutions to aggregate information and collaborate with a master model.
“Federated learning offers tremendous promise,” said Marius George Linguraru, D.Phil., M.A., M.Sc., the Connor Family Professor of Research and Innovation, principal investigator at the Sheikh Zayed Institute of Pediatric Surgical Innovation and senior author on the manuscript. “As a community of experts, we have found that federated learning allows us to move away from the challenges of sharing data in central repositories. Instead, we share the models, which can be designed to protect privacy by limiting what’s shared outside of any given institution.”
A champion of pediatric health, Linguraru wants to ensure that children are represented in the development of models that advance science and medicine. “Sharing data is even more crucial when there are few patients, such as in rare diseases or pediatric populations,” he said. “In general, healthcare data suffers from inequitable representation in our public health systems and services.”
Learn more here about the challenges and potential solutions from experts at Rhino Health, Johns Hopkins University School of Medicine, NVIDIA, University of Cambridge, Ben-Gurion University Israel, MD Anderson Cancer Center, Dana-Farber Cancer Institute and Children’s National Hospital.
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Children’s National Hospital scientists won first place in a global competition to use artificial intelligence (AI) to analyze pediatric brain tumor volumes, demonstrating the team’s ground-breaking advances in imaging and machine learning.
During the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), the Children’s National team demonstrated the most accurate algorithm to study the volume of brain tumors – the most common solid tumors affecting children and adolescents and a leading cause of disease-related death at this young age. The technology could someday help oncologists understand the extent of a patient’s disease, quantify the efficacy of treatments and predict patient outcomes.
“The Brain Tumor Segmentation Challenge inspires leaders in medical imaging and deep learning to try to solve some of the most vexing problems facing radiologists, oncologists, computer engineers and data scientists,” said Marius George Linguraru, D.Phil., M.A., M.Sc., the Connor Family Professor in Research and Innovation and principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation. “I am honored that our team won, and I’m even more thrilled for our clinicians and their patients, who need us to keep moving forward to find new ways to treat pediatric brain tumors.”
Why we’re excited
With roughly 4,000 children diagnosed yearly, pediatric brain tumors are consistently the most common type of pediatric solid tumor, second only to leukemia in pediatric malignancies. At the urging of Linguraru and one of his peers at the Children’s Hospital of Philadelphia, pediatric data was included in the international competition for the first time, helping to ensure that children are represented in medical and technological advances.
The contest required participants to use data from multiple institutions and consortia to test competing methods fairly. The Children’s National team created a method to tap into the power of two types of imaging and machine learning: 3D convolutional neural network and 3D Vision Transformer-based deep learning models. They identified regions of the brain affected by tumors, made shrewd data-processing decisions driven by the team’s experience in AI for pediatric healthcare and achieved state-of-the-art results.
The competition drew 18 teams who are leaders from across the AI and machine learning community. The runner-up teams were from NVIDIA and the University of Electronic Science and Technology of China.
The big picture
“Children’s National has an all-star lineup, and I am thrilled to see our scientists recognized on an international stage,” said interim Executive Vice President and Chief Academic Officer Catherine Bollard, M.D., M.B.Ch.B., director of the Center for Cancer for Immunology Research. “As we work to attack brain tumors from multiple angles, we continue to show our exceptional ability to create new and better tools for diagnosing, imaging and treating these devastating tumors.”
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Researchers at Children’s National Hospital are embarking on an effort to deploy computer vision and artificial intelligence (AI) to ensure medical professionals appropriately use personal protective equipment (PPE). This strikingly common problem touches almost every medical specialty and setting.
With nearly $2.2 million in grants from the National Institutes of Health, the team is combining their expertise with information scientists at Drexel University and engineers at Rutgers University to build a system that will alert doctors, nurses and other medical professionals of mistakes in how they are wearing their PPE. The goal is to better protect healthcare workers (HCWs) from dangerous viruses and bacteria that they may encounter — an issue laid bare with the COVID-19 pandemic and PPE shortages.
“If any kind of healthcare setting says they don’t have a problem with PPE non-adherence, it’s because they’re not monitoring it,” said Randall Burd, M.D., Ph.D., division chief of Trauma and Burn Surgery at Children’s National and the principal investigator on the project. “We need to solve this problem, so the medical community will be prepared for the next potential disaster that we might face.”
The big picture
The World Health Organization has estimated that between 80,000 and 180,000 HCWs died globally from COVID-19 between January 2020 and May 2021 — an irreplaceable loss of life that created significant gaps in the pandemic response. Research has shown that HCWs had an 11-fold greater infection risk than the workers in other professions, and those who were not wearing appropriate PPE had a 1/3 higher infection risk, compared to peers who followed best practices.
Burd said the Centers for Disease Control and Prevention has recommended that hospitals task observers to stand in the corner with a clipboard to watch clinicians work and confirm that they are being mindful of their PPE. However, “that’s just not scalable,” he said. “You can’t always have someone watching, especially when you may have 50 people in and out of an operating room on a challenging case. On top of that, the observers are generally trained clinicians who could be filling other roles.”
What’s ahead
Bringing together the engineering talents at Drexel and Rutgers with the clinical and machine-learning expertise at Children’s National, the researchers plan to build a computer-vision system that will watch whether HCWs are properly wearing PPE such as gloves, masks, eyewear, gowns and shoe covers.
The team is contemplating how the system will alert HCWs to any errors and is considering haptic watch alerts and other types of immediate feedback. The emerging power of AI brings tremendous advantages over the current, human-driven systems, said Marius George Linguraru, D.Phil., M.A., M.Sc., the Connor Family Professor in Research and Innovation at Children’s National and principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation.
“Human observers only have one pair of eyes and may fatigue or get distracted,” Linguraru said. “Yet artificial intelligence, and computers in general, work without getting tired. We are excited to figure out how a computer can do this work – without ever blinking.”
Children’s National Hospital leads the way
Linguraru says that Children’s National and its partners make up the ideal team to tackle this vexing challenge because of their ability to assemble a multidisciplinary team of scientists and engineers who can work together with clinicians. “This is a dialogue,” he said. “A computer scientist, like myself, needs to understand the intricacies of complicated clinical realities, while a clinician needs to understand how AI can impact the practice of medicine. The team we are bringing together is intentional and poised to fix this problem.”
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Children’s National Hospital is joining a team of global health researchers to use large language models (LLMs) like ChatGPT to help Kenyan youth learn about their health and adopt lifestyles that may prevent cancer, diabetes and other non-communicable diseases.
The work, which is one of nearly 50 Grand Challenges Catalyzing Equitable Artificial Intelligence (AI) Use grants announced by the Bill & Melinda Gates Foundation, will harness the emerging power of AI to empower young people with information that they can carry through adulthood to reduce rates of unhealthy behaviors including physical inactivity, unhealthy diet and use of tobacco and alcohol.
“We are thrilled to be part of this effort to bring our AI expertise closer to young patients who would benefit dramatically from technology and health information,” said Marius George Linguraru, D.Phil., M.A., M.Sc., a co-principal investigator for the project, the Connor Family Professor in Research and Innovation at Children’s National and principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation. “Using generative AI, we will build an application to enhance the knowledge, attitudes and healthy habits of Kenyan youth and use this as a foundation to improve health inequities around the globe.”
Why it matters
A lower middle-income country located on the east coast of Sub-Saharan Africa, Kenya is home to 50 million people and one of the continent’s fastest-growing economies. English is one of Kenya’s official languages, and the country has been recognized as a technology leader in Africa, with 82% of Kenyans having phone connectivity. Taken together, these factors make the country an ideal location to deploy an LLM-based platform designed to improve health information and attitudes.
The Gates Foundation selected this project from more than 1,300 grant applications. The nearly 50 funded projects are aimed at supporting low- and middle-income countries to harness the power of AI for good and help countries participate in the AI development process. The project’s findings will contribute to building an evidence base for testing LLMs that can fill wide gaps in access and equitable use of these tools. Each of the grants provides an opportunity to mitigate challenges experienced by communities, researchers and governments.
What’s next
The project development will be led by the National Cancer Institute of Kenya, with Linguraru and other global experts advising the effort from Kenyan institutions and Stanford University. Researchers plan to enroll youth from universities, shopping malls, markets, sporting events and other high-traffic locations. The study will look at participants’ risk factors and how their attitudes toward healthier lifestyles changed after engaging with the new LLM platform.
“The team is thrilled to be selected as one of the nearly 50 most promising AI proposals in the Gates Foundation Grand Challenge competition, and we look forward to seeing how our work can benefit the health of Kenyan youth,” said Dr. Martin Mwangi, principal investigator for the project and head of the Cancer Prevention and Control Directorate at the National Cancer Institute of Kenya. “If successful, we hope to share this model and the expertise we gain to expand health equity and knowledge to other regions.”
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“Artificial Intelligence may be the greatest tool we have for improving the quality of and access to medical care for children, especially those most vulnerable to health system inequities,” said Dr. Linguraru. “This professorship will help me extend our leadership in this vital field. The tools and care strategies we develop will benefit children worldwide.”
Dr. Linguraru is a principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National. He directs the award-winning Precision Medicine Imaging Group. He is also a professor of Radiology and Pediatrics and secondary professor of Biomedical Engineering at George Washington University.
About the award
Dr. Linguraru joins a distinguished group of 42 Children’s National physicians and scientists who hold an endowed chair. Professorships at Children’s National support groundbreaking work on behalf of children and their families and foster new discoveries and innovations in pediatric medicine. These appointments carry prestige and honor that reflect the recipient’s achievements and donor’s forethought to advance and sustain knowledge.
Dr. Linguraru is a global leader in harnessing the power of quantitative imaging and machine learning to rapidly and positively impact children’s health. Dr. Linguraru and his team use artificial intelligence (AI) and digital technology innovations to improve access to healthcare and the understanding of rare and newborn diseases. Their work enables clinicians to deliver care faster, evaluate responses to treatments and prevent health complications. They have positioned Children’s National as an international leader in the development of pediatric AI to ensure equitable care for all children.
“Artificial Intelligence may be the greatest tool we have for improving the quality of and access to medical care for children, especially those most vulnerable to health system inequities,” said Dr. Linguraru. “This professorship will help me extend our leadership in this vital field. The tools and care strategies we develop will benefit children worldwide.”
About the donors
The Connor family, through their vision and generosity, are ensuring that Dr. Linguraru and future holders of this professorship will launch bold, new initiatives to rapidly advance the field of pediatric research and innovation, elevate our leadership and improve the lifetimes of children.
“We strongly believe in the power of academic entrepreneurship to improve the health and wellbeing of children,” said Ed and Chris Connor, who are longtime donors and members of the Children’s National community. “This endowment is our way of supporting Children’s National’s work in research and innovation and recognizing Dr. Linguraru’s international leadership in using AI to benefit child health.”
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The daylong event drew experts from the Food and Drug Administration, Pfizer, Oracle Health, NVIDIA, AWS Health and elsewhere to start building a community aimed at using data for the advancement of pediatric medicine.
The future of pediatric medicine holds the promise of artificial intelligence (AI) that can help diagnose rare diseases, provide roadmaps for safer surgeries, tap into predictive technologies to guide individual treatment plans and shrink the distance between patients in rural areas and specialty care providers.
These and dozens of other innovations were contemplated as scientists came together at the inaugural symposium on AI in Pediatric Health and Rare Diseases, hosted by Children’s National Hospital and the Fralin Biomedical Research Institute at Virginia Tech. The daylong event drew experts from the Food and Drug Administration, Pfizer, Oracle Health, NVIDIA, AWS Health and elsewhere to start building a community aimed at using data for the advancement of pediatric medicine.
“AI is the single greatest tool for improving equity and access to health care,” said symposium host Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator at the Sheikh Zayed Institute for Pediatric Surgical Innovation. “As a population, kids are vastly underrepresented in scientific research and resulting treatments, but pediatric specialties can use AI to provide medical care to kids more efficiently, more quickly and more effectively.”
What they’re saying
Scientists shared their progress in building digital twins to predict surgical outcomes, enhancing visualization to increase the precision of delicate interventions, establishing data command centers to anticipate risks for fragile patients and more. Over two dozen speakers shared their vision for the future of medicine, augmented by the power of AI:
Keynote speaker Subha Madhavan, Ph.D., vice president and head of AI and machine learning at Pfizer, discussed various use cases and the potential to bring drugs to market faster using real-world evidence and AI. She saw promise for pediatrics. “This is probably the most engaging mission: children’s health and rare diseases,” she said. “It’s hard to find another mission that’s as compelling.”
Brandon J. Nelson, Ph.D., staff fellow in the Division of Imaging, Diagnostics and Software Reliability at the Food and Drug Administration, shared ways AI will improve diagnostic imaging and reduce radiation exposure to patients, using more advanced image reconstruction and denoising techniques. “That is really our key take-home message,” he said. “We can get what … appear as higher dose images, but with less dose.”
Daniel Donoho, M.D., a neurosurgeon at Children’s National, introduced the audience to the potential of “Smart ORs”: operating rooms where systems can ingest surgery video and provide feedback and skill assessments. “We have to transform the art of surgery into a measurable and improvable scientific practice,” he said.
Debra Regier, M.D., chief of Genetics and Metabolism at Children’s National, discussed how AI could be used to diagnose and treat rare diseases by conducting deep dives into genetics and studying dysmorphisms in patients’ faces. Already, Children’s National has designed an app – mGene – that measures facial features and provides a risk score to help anyone in general practice determine if a child has a genetic condition. “The untrained eye can stay the untrained eye, and the family can continue to have faith in their provider,” she said.
What’s next
Linguraru and others stressed the need to design AI for kids, rather than borrow it from adults, to ensure medicine meets their unique needs. He noted that scientists will need to solve challenges, such as the lack of data inherent in rare pediatric disorders and the simple fact that children grow. “Children are not mini-adults,” Linguraru said. “There are big changes in a child’s life.”
The landscape will require thoughtfulness. Naren Ramakrishnan, Ph.D., director of the Sanghani Center for Artificial Intelligence & Analytics at Virginia Tech and symposium co-host, said that scientists are heading into an era with a new incarnation of public-private partnerships, but many questions remain about how data will be shared and organizations will connect. “It is not going to be business as usual, but what is this new business?” he asked.
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Syed Anwar, Ph.D., is developing self-supervised algorithms for medical imaging.
Syed Anwar, Ph.D., joins the growing AI initiative in the Sheikh Zayed Institute for Pediatric Surgical Innovation (SZI) at Children’s National Hospital with extensive research experience in machine learning and medical imaging from the University of Engineering and Technology in Taxila, Pakistan, the University of Sheffield, U.K., and the University of Central Florida through the Fulbright Scholars Program. At Children’s National, he’s grateful for the proximity between researchers and clinicians as he studies federated learning and works to build “digital twins” that allow medical teams to test complicated surgical and treatment plans on infants with disorders including Pierre Robin Sequence. This rare congenital birth defect is characterized by an underdeveloped jaw, backward displacement of the tongue and upper airway obstruction. Anwar works alongside Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator at SZI, and the Precision Medical Imaging Lab to increase AI capacity in all areas of pediatric care at the hospital.
Q: What is the focus of your research work?
A: The main theme is a digital twin. It’s an engineering innovation that people have been using for some time, especially in manufacturing and aviation. For example, you can create a digital simulation of an airplane with a flight simulator. Now, people are starting to use the power of data-driven digital twins for medical applications.
I’m working to create a digital twin for infants born with Pierre Robin Sequence, where they need to have surgical interventions for improving the structure of the bones in the jaws. It includes a lot of clinical approaches, including surgery and ways to address apnea and food intake.
There are multiple areas of clinical expertise involved. With a digital twin, we will have a digital representation of the patient, and the surgeon, the radiologist and other clinicians can experiment with a proposed intervention before actually touching the patient.
Syed Anwar, Ph.D., joins the growing AI initiative in the Sheikh Zayed Institute for Pediatric Surgical Innovation (SZI) at Children’s National Hospital.
Q: How else are you using your engineering background in your research?
A: Another part of my work is federated learning, which is a type of machine learning. In artificial intelligence, we want big data as the starting point to train our deep learning models. When studying children, this is not always possible because we have smaller data sets.
Federated learning is a tool that helps in these situations. Data is kept at a local site. We train a model to learn from all that data at the different sites. One benefit is that we don’t need to share the data, which is very useful for preserving patient privacy. But you can still apply deep learning models and develop AI solutions using the distributed data for improved clinical outcomes.
Q: What do you see as the main hurdles you have to overcome?
A: For all medical data, and particularly for kids, the amount of data we see in a children’s hospital is small, particularly for rare diseases.
The second hurdle is good, quality labels. For example, if you are doing tumor segmentation, you still need to have some ground rules from a radiologist showing which part of the image is the tumor.
These challenges come together in another focus of my research – self-supervised learning, meaning we can train a machine to learn from the data itself, without the labels or ground rules. From a machine learning point of view, I am in the process of developing self-supervised algorithms for medical imaging and in general for medical data. It’s an amazing time to be in this research area and to enable the translation of AI driven solutions for clinical workflows.
Q: What excites you about being at Children’s National and working at SZI?
A: I come from an engineering background, and my research area has been medical imaging for some time, mainly magnetic resonance imaging. Before coming here, I was working at a university in Pakistan, teaching machine learning and conducting research related to medical imaging and biomedical signal processing. But I was missing strong connections with people caring for patients at the hospital.
https://innovationdistrict.childrensnational.org/wp-content/uploads/2022/11/MRI-feature.png300400Innovation Districthttps://innovationdistrict.childrensnational.org/wp-content/uploads/2023/12/innovationdistrict_logo-1-1030x165.pngInnovation District2022-11-03 16:00:562022-11-03 22:05:34Building “digital twins” to test complicated surgeries
New research shows that artificial intelligence can be rapidly designed to study the lung images of COVID-19 patients.
Artificial intelligence can be rapidly designed to study the lung images of COVID-19 patients, opening the door to the development of platforms that can provide more timely and patient-specific medical interventions during outbreaks, according to research published this month in Medical Image Analysis.
The findings come as part of a global test of AI’s power, called the COVID-19 Lung CT Lesion Segmentation Challenge 2020. More than 2,000 international teams came together to train the power of machine learning and imaging on COVID-19, led by researchers at Children’s National Hospital, AI tech giant NVIDIA and the National Institutes of Health (NIH).
The bottom line
Many of the competing AI platforms were successfully trained to analyze lung lesions in COVID-19 patients and measure acute issues including lung thickening, effusions and other clinical findings. Ten leaders were named in the competition, which ran between November and December 2020. The datasets included patients with a range of ages and disease severity.
Yet work remains before AI could be implemented in a clinical setting. The AI models performed comparably to radiologists when analyzing data similar to what the algorithms had already encountered. However, the AI was less valuable when trained on fresh data from other sources during the testing phase, indicating that systems may need to study larger and more diverse data sets to meet their full potential. This is a challenge with AI that has been noted by others too.
What they’re saying
“These are the first steps in learning how we can quickly and accurately train AI for clinical use,” said 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. “The global interest in COVID-19 gave us a groundbreaking opportunity to address a health crisis, and multidisciplinary teams can now focus that interest and energy on developing better tools and methods.”
Holger Roth, senior applied research scientist at NVIDIA, said the challenge gave researchers around the world a shared platform for developing and evaluating AI algorithms to quickly detect and quantify COVID lesions from lung CT images. “These models help researchers visualize and measure COVID-specific lesions of infected patients and can facilitate timelier and patient-specific medical interventions to better treat COVID,” he said.
Moving the field forward
The organizers see great potential for clinical use. In areas with limited resources, AI could help triage patients, guide the use of therapeutics or provide diagnoses when expensive testing is unavailable. AI-defined standardization in clinical trials could also uniformly measure the effects of the countermeasures used against the disease.
Linguraru and his colleagues recommend more challenges, like the lung segmentation challenge, to develop AI applications in biomedical spaces that can test the functionality of these platforms and harness their potential. Open-source AI algorithms and public curated data, such as those offered through the COVID-19 Lung CT Lesion Segmentation Challenge 2020, are valuable resources for the scientific and clinical communities to work together on advancing healthcare.
“The optimal treatment of COVID-19 and other diseases hinges on the ability of clinicians to understand disease throughout populations – in both adults and children,” Linguraru said. “We are making significant progress with AI, but we must walk before we can run.”
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Researchers at Children’s National Hospital have created a new artificial intelligence (AI) algorithm that promises to be as successful at detecting early signs of rheumatic heart disease (RHD) in color Doppler echocardiography clips as expert clinicians.
Researchers at Children’s National Hospital have created a new artificial intelligence (AI) algorithm that promises to be as successful at detecting early signs of rheumatic heart disease (RHD) in color Doppler echocardiography clips as expert clinicians. Even better, this novel model diagnoses this deadly heart condition from echocardiography images of varying quality — including from low-resource settings — a huge challenge that has delayed efforts to automate RHD diagnosis for children in these areas.
Why it matters
Current estimates are that 40.5 million people worldwide live with rheumatic heart disease, and that it kills 306,000 people every year. Most of those affected are children, adolescents and young adults under age 25.
Though widely eradicated in nations such as the United States, rheumatic fever remains prevalent in developing countries, including those in sub-Saharan Africa. Recent studies have shown that, if detected soon enough, a regular dose of penicillin may slow the development and damage caused by RHD. But it has to be detected.
The hold-up in the field
Diagnosing RHD requires an ultrasound image of the heart, known as an echocardiogram. However, ultrasound in general is very variable as an imaging modality. It is full of texture and noise, making it one of the most challenging to interpret visually. Specialists undergo significant training to read them correctly. However, in areas where RHD is rampant, people who can successfully read these images are few and far between. Making matters worse, the devices used in these low resource settings have their own levels of varying quality, especially when compared to what is available in a well-resourced hospital elsewhere.
The research team hypothesized that a novel, automated deep learning-based method might detect successfully diagnose RHD, which would allow for more diagnoses in areas where specialists are limited. However, to date, machine learning has struggled the same way the human eye does with noisy ultrasound images.
Children’s National leads the way
Using approaches that led to successful objective digital biometric analysis software for non-invasive screening of genetic disease, researchers at the Sheikh Zayed Institute for Pediatric Surgical Innovation, including medical imaging scientist Pooneh Roshanitabrizi, Ph.D., and Marius Linguraru, D.Phil., M.A., M.Sc., principal investigator, partnered with clinicians from Children’s National Hospital, including Craig Sable, M.D., associate chief of Cardiology and director of Echocardiography, and cardiology fellow Kelsey Brown, M.D., who are heavily involved in efforts to research, improve treatments and ultimately eliminate the deadly impacts of RHD in children. The collaborators also included cardiac surgeons from the Uganda Heart Institute and cardiologists from Cincinnati Children’s Hospital Medical Center.
Dr. Linguraru’s team of AI and imaging scientists spent hours working with cardiologists, including Dr. Sable, to truly understand how they approach and assess RHD from echocardiograms. Building the tool based on that knowledge is why this tool stands apart from other efforts to use machine-learning for this purpose. Orienting the approach to the clinical steps of diagnosis is what led to the very first deep learning algorithm that diagnoses mild RHD with similar success to the specialists themselves. After the platform was built, 2,136 echocardiograms from 591 children treated at the Uganda Heart Institute fed the learning algorithm.
What’s next
The team will continue to collect data points based on clinical imaging data to refine and validate the tool. Ultimately, researchers will look for a way that the algorithm can work directly with ultrasound/echocardiogram machines. For example, the program might be run through an app that sits on top of an ultrasound device and works on the same platform to communicate directly with it, right in the clinic. By putting the two technologies together, care providers on the ground will be able to diagnose mild cases and prescribe prophylactic treatments like penicillin in one visit.
A new AI algorithm can detect subtle brain abnormalities that cause epileptic seizures.
An artificial intelligence (AI) algorithm that can detect subtle brain abnormalities that cause epileptic seizures has been developed by a UCL-led team of international researchers, including Children’s National Hospital.
To do this, the team quantified features from MRI scans, such as how thick or folded the brain was at nearly 300,000 locations in each case.
They then trained the AI algorithm using examples labelled by expert radiologists as either a healthy brain or one with focal cortical dysplasia (FCD) based on their patterns and features.
The results, published in Brain, showed that in the main cohort of 538 patients, the algorithm was able to detect the FCD in 67% of cases.
“We put an emphasis on creating an AI algorithm that was interpretable and could help doctors make decisions. Showing doctors how the Multicentre Epilepsy Lesion Detection project (MELD) algorithm made its predictions was an essential part of that process,” said Mathilde Ripart, research assistant at UCL and the study’s co-first author.
Around 1% of the population have epilepsy and, of these, 20-30% do not respond to medications.
“We are excited to collaborate with MELD on ways to improve the treatment of pharmacoresistant epilepsy,” said Nathan Cohen, M.D., neurologist at Children’s National Hospital and co-author of the study. “This advanced imaging platform is open source and demonstrates the benefit of team science at the broadest scale.”
In children who have had surgery to control their epilepsy, FCD is the most common cause, and in adults it is the third most common cause.
Additionally, of patients who have epilepsy that have an abnormality in the brain that cannot be found on MRI scans, FCD is the most common cause.
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The scientific community continues to debate AI’s possibility of outperforming humans in specific tasks. In the context of the machine’s performance versus the clinician, Linguraru et al. argue that the community must consider social, psychological and economic contexts in addition to the medical implications to answer this puzzling question.
In a special report published in Radiology: Artificial Intelligence, a Children’s National Hospital expert and other institutions discussed a shared multidisciplinary vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence (AI).
“AI algorithms can construct, reconstruct and interpret radiologic images, but they also have the potential to guide the scanner and optimize its parameters,” 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. “The acquisition and analysis of radiologic images is personalized, and radiologists and technologists adapt their approach to every patient based on their experience. AI can simplify this process and make it faster.”
The scientific community continues to debate AI’s possibility of outperforming humans in specific tasks. In the context of the machine’s performance versus the clinician, Linguraru et al. argue that the community must consider social, psychological and economic contexts in addition to the medical implications to answer this puzzling question.
Still, they believe that developing a useful radiologic AI system designed with the participation of radiologists could complement and possibly surpass human’s interpretation of the visuals.
Given AI’s potential applications, the authors encouraged radiologists to access many freely available resources to learn about machine learning, and radiomics to familiarize with basic concepts. Coursera, for example, can teach radiologists about convolutional neural networks and other techniques used by AI researchers.
Conversely, AI experts must reach out to radiologists and participate in public speaking events about their work. According to the researchers, during those engagement opportunities, clinicians understood the labor-saving benefits of automatic complex measurements on millions of images—something that they have been doing manually for years.
There are also hurdles around this quest of automation, which Linguraru et al. hope both fields can sort out by working together. A critical challenge that the experts mentioned was earning the trust of clinicians that are skeptical about the “black box” functionality of AI models, which makes it hard to understand and explain the behavior of a model.
Some questions, too, need answers on how to best leverage both human intelligence and AI by using human-in-the-loop where people train, tune, and test a particular algorithm, or AI in-the-loop where this different framing generates AI input and reflection in human systems.
“The key is to have a good scientific premise to adequately train and validate the algorithms and make them clinically useful. At that point, we can trust the box,” said Linguraru. “In radiology, we should focus on AI systems with radiologists in-the-loop, but also on training radiologists with AI in-the-loop, particularly as AI systems are getting smarter and learning to work better with radiologists.”
The experts also provided possible solutions to sharing large datasets, how to build datasets that allows robust investigations and how to improve the quality of a model that might be compared against human’s gold standard.
This special report is the second in a series of panel discussions hosted by the Radiological Society of North America and the Medical Image Computing and Computer Assisted Intervention Society. The discussion builds upon the first in the series “Machine Learning for Radiology from Challenges to Clinical Applications” that touched on how to incentivize annotators to participate in projects, the promotion of “team science” to address research questions and challenges, among other topics.
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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:
Shishuai Hu, et al. Northwestern Polytechnical University, China. “Semi-supervised Method for COVID-19 Lung CT Lesion Segmentation”
Fabian Isensee, et al. German Cancer Research Center, Germany. “nnU-Net for Covid Segmentation”
Claire Tang, Lynbrook High School, USA. “Automated Ensemble Modeling for COVID-19 CT Lesion Segmentation”
Qinji Yu, et al. Shanghai JiaoTong University, China. “COVID-19-20 Lesion Segmentation Based on nnUNet”
Andreas Husch, et al. University of Luxembourg, Luxembourg. “Leveraging State-of-the-Art Architectures by Enriching Training Information – a case study”
Tong Zheng, et al. Nagoya University, Japan. “Fully-automated COVID-19-20 Segmentation”
Vitali Liauchuk. United Institute of Informatics Problems (UIIP), Belarus. “Semi-3D CNN with ImageNet Pretrain for Segmentation of COVID Lesions on CT”
Ziqi Zhou, et al. Shenzhen University, China. “Automated Chest CT Image Segmentation of COVID-19 with 3D Unet-based Framework”
Jan Hendrik Moltz, et al. Fraunhofer Institute for Digital Medicine MEVIS, Germany. “Segmentation of COVID-19 Lung Lesions in CT Using nnU-Net”
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.”
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As a Johnson & Johnson Innovation Quickfire Children’s Challenge awardee, Dr. Finkel and AlgometRx will be among the first group of startups taking up residence at the new JLABS @ Washington, DC, located on the Children’s National Research & Innovation Campus, when it opens in 2021 at the historic former Walter Reed Army Medical Center site.
Medical technology innovator Julia Finkel, M.D., principal investigator for the Pain Medicine Initiative of the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital, recently participated in Galen Growth’s 2020 Global Healthtech Summit on a virtual panel featuring resident companies from Johnson & Johnson Innovation – JLABS who are utilizing artificial intelligence (AI) with the aim to create advanced solutions for diagnostics, treatment and clinical trials. The summit, hosted in Singapore, brought the innovators together to discuss their views on their progress, the challenges and opportunities for bringing medtech innovations to market in the current climate, as well as the tools needed to succeed.
Joining Dr. Finkel on the panel were JLABS resident company leaders Don Crawford, CEO, Analytics 4 Life; Jim Havelka, CEO, Inform AI; and Kim Walpole, CEO, Trials.ai, which leverages AI to help research teams design more effective clinical trials. The 50-minute program, moderated by Kara Bortone, senior director, Portfolio and Sourcing Management, Johnson & Johnson Innovation – JLABS, focused on topics such as how these startups approached the market and regulatory processes as well as the up-and-coming trends in health technology.
A pediatric anesthesiologist, Dr. Finkel explained the significance of achieving real-time, objective pain measurement. “Pain is one word that represents a myriad of conditions,” she says. “Pain from acute post-operative conditions is very different from peripheral neuropathic pain and different from the type of inflammatory pain seen in lupus and rheumatoid arthritis. Being able to discern the drivers of pain, the etiology, is essential to treating it well and to developing better therapeutics in the future.”
Dr. Finkel points out that AlgometRx measures nociception, which is pain fiber activation, and that is also what medications are addressing. “We’re not discounting a patient’s perception of pain, as we recognize that one’s experience of pain is very complex,” she says. “What we aim to measure is the activity being transmitted by the pain nerve and the type of nerve fiber that is doing the transmitting.”
Aiming to identify pain phenotypes is an important part of current AlgometRx development work, says Dr. Finkel, as it could significantly aid clinical decision-making in treating and monitoring patients’ pain. The company’s current regulatory focus is to seek FDA clearance related to its potential use for patients with peripheral neuropathy, which is pain and numbness resulting from damage to the nerves outside of the brain and spinal cord. The company has also identified fibromyalgia cases as a place where the technology could potentially benefit a large number of patients as it considers regulatory clearance targets.
As the COVID-19 pandemic presented many unique challenges to healthcare startups this year, panel participants were asked to discuss the hurdles they faced and how it impacted device development.
Dr. Finkel notes that the pandemic slowed patient enrollment in AlgometRx clinical studies, but also presented some upside. “At first, that had a negative impact, but it wound up being a good thing,” she says. “It gave us a moment to pause, regroup and examine the data we’d already generated. That break gave us improved information and a new, more powerful approach. It changed our trajectory by altering our regulatory path in terms of the order of things in our pipeline, so we’ve been enormously productive.”
As a Johnson & Johnson Innovation Quickfire Children’s Challenge awardee, Dr. Finkel and AlgometRx will be among the first group of startups taking up residence at the new JLABS @ Washington, DC, located on the Children’s National Research & Innovation Campus, when it opens in 2021 at the historic former Walter Reed Army Medical Center site. Along with a one-year residency at the new JLABS @ Washington DC facility,* AlgometRx will receive mentorship from experts at the Johnson & Johnson Family of Companies and grant funding to help support its continued advancement to commercialization.
*Residency at JLABS @ Washington subject to acceptance and execution of a License Agreement with Children’s National.
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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.
“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.
“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.
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The telehealth command center located a few steps away from the cardiac ICU at Children’s National Hospital.
The cardiac critical care team at Children’s National Hospital has developed an innovative Tele-Cardiac Critical Care model aiming to keep constant watch over the most fragile children with critical heart disease in the cardiac ICU. The system combines traditional remote monitoring and video surveillance with an artificial intelligence algorithm trained to flag early warning signs that a critically ill infant may suffer a serious event like cardiac arrest while recovering from complex cardiac surgery. This second set of eyes helps bedside teams improve patient safety and quality of care.
These high risk post-operative patients are often neonates or small infants born with the most complex and critical congenital heart diseases that require surgery or interventional cardiac catheterization in their first days or weeks of life. At these early stages after crucial cardiac surgery, these patients can decompensate dangerously fast with few outward physical symptoms.
The AI algorithm (T3) monitors miniscule changes in oxygen delivery and identifies any mismatch with a child’s oxygen needs. It also tracks and displays small changes in vital sign trends that could lead to a serious complication. The cardiac ICU command center staff then analyzes additional patient data and alerts the bedside team whenever needed.
The Tele-Cardiac Critical Care program started two years ago. In that time, the program has contributed to a significant decrease in post-operative cardiac arrest for this patient population.
“It’s easy to see how a model like this could be adapted to other critical care scenarios, including our other intensive care units and even to adult units,” says Ricardo Munoz, M.D., chief of Cardiac Critical Care and executive director of Telehealth. It allows the physicians and nurses to keep constant watch over these fragile patients without requiring a physician to monitor every heartbeat in person for every patient at every hour of the day to maintain optimal outcomes for all of them.”
Dr. Munoz and Alejandro Lopez-Magallon, M.D., medical director of Telehealth and cardiac critical care specialist, presented data from the pilot program at the American Telemedicine Association’s virtual Annual Meeting on June 26, 2020.
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“By advancing our understanding of the genetic causes and the anatomic differences among patients, the real goal of this research is to generate knowledge that will allow us to take better care of children with hypospadias,” Daniel Casella, M.D. says.
Daniel Casella, M.D., a urologist at Children’s National, was honored with an AUA Mid-Atlantic Section William D. Steers, M.D. Award, which provides two years of dedicated research funding that he will use to better understand the genetic causes for hypospadias.
With over 7,000 new cases a year in the U.S., hypospadias is a common birth defect that occurs when the urethra, the tube that transports urine out of the body, does not form completely in males.
Dr. Casella has identified a unique subset of cells in the developing urethra that have stopped dividing but remain metabolically active and are thought to represent a novel signaling center. He likens them to doing the work of a construction foreman. “If you’re constructing a building, you need to make sure that everyone follows the blueprints. We believe that these developmentally senescent cells are sending important signals that define how the urethra is formed,” he says.
His project also will help to standardize the characterization of hypospadias. Hypospadias is classically associated with a downward bend to the penis, a urethra that does not extend to the head of the penis and incomplete formation of the foreskin. Still, there is significant variability among patients’ anatomy and to date, no standardized method for documenting hypospadias anatomy.
“Some surgeons take measurements in the operating room, but without a standardized classification system, there is no definitive way to compare measurements among providers or standardize diagnoses from measurements that every surgeon makes,” he adds. “What one surgeon may call ‘distal’ may be called ‘midshaft’ by another.” (With distal hypospadias, the urethra opening is near the penis head; with midshaft hypospadias, the urethra opening occurs along the penis shaft.)
“By advancing our understanding of the genetic causes and the anatomic differences among patients, the real goal of this research is to generate knowledge that will allow us to take better care of children with hypospadias,” he says.
Parents worry about lingering social stigma, since some boys with hypospadias are unable to urinate while standing, and in older children the condition can be associated with difficulties having sex. Surgical correction of hypospadias traditionally is performed when children are between 6 months to 1 year old.
When reviewing treatment options with family, “discussing the surgery and postoperative care is straight forward. The hard part of our discussion is not having good answers to questions about long-term outcomes,” he says.
Dr. Casella’s study hopes to build the framework to enable that basic research to be done.
“Say we wanted to do a study to see how patients are doing 15-20 years after their surgery. If we go to their charts now, often we can’t accurately describe their anatomy prior to surgery. By establishing uniform measurement baselines, we can accurately track long-term outcomes since we’ll know what condition that child started with and where they ended up,” he says.
Dr. Casella’s research project will be conducted at Children’s National under the mentorship of Eric Vilain, M.D., Ph.D., an international expert in sex and genitalia development; Dolores J. Lamb, Ph.D., HCLD, an established leader in urology based at Weill Cornell Medicine; and Marius George Linguraru, DPhil, MA, MSc, an expert in image processing and artificial intelligence.
https://innovationdistrict.childrensnational.org/wp-content/uploads/2019/03/DNA-Molecule.png300400Innovation Districthttps://innovationdistrict.childrensnational.org/wp-content/uploads/2023/12/innovationdistrict_logo-1-1030x165.pngInnovation District2019-04-23 12:45:022023-10-23 10:09:31Decoding cellular signals linked to hypospadias
Advanced MRI visualization techniques to follow blood flow in the hearts of cardiac patients. Gene therapy for pediatric patients with Duchenne muscular dystrophy. 3D-printed casts for treating clubfoot. These were among the most popular articles we published on Innovation District in 2023. Read on for our full list.
Children’s National Hospital is joining a team of global health researchers to use large language models (LLMs) like ChatGPT to help Kenyan youth learn about their health and adopt lifestyles that may prevent cancer, diabetes and other non-communicable diseases.
