Tag Archive for: AI

illustration of neurons

New AI platform accelerates brain inflammation research 10,000-fold

illustration of neurons

A new AI tool developed by Children’s National and Howard University analyzes brain immune cells 10,000x faster than manual methods.

A new Machine Learning and Artificial Intelligence tool from researchers at Children’s National Hospital (CNH) and Howard University (HU) accelerates discoveries in brain inflammation. Called StainAI, it rapidly and accurately analyzes microglia, the brain’s immune cells. Scientists currently analyze microglia slowly by hand. StainAI automates this process and speeds it up 10,000-fold. Its use will aid discovery of new treatments for inflammatory brain conditions such as infection, autoimmunity, and aging.

Solving a problem

Traditionally, scientists study microglia one cell at a time. They reconstruct each cell’s shape by hand under a microscope. The shape helps classify microglia as “resting” (normal) or “activated” (inflamed). The manual process is tedious and slow. It limits analyses to a few microglia in small brain areas.

StainAI changes that. It uses deep machine learning and artificial intelligence to overcome and exceed the manual method’s limitations. It correctly classifies millions of microglia from standard microscopic images. StainAI also localizes each microglia to its brain region in 3D. These features enable single-cell analyses of immune activity at a scale not feasible before – the entire brain.

A tool with broad impact

The team applied StainAI to two models of brain injury and inflammation to show its utility. In a rodent model of pediatric cardiac arrest, StainAI identified new brain regions susceptible to injury. In a simian model of viral infection, StainAI localized rod-shaped microglia normally found in white matter to an unexpected brain region – the hippocampus.  These findings point towards new treatments and highlight StainAI’s value across diseases and species.

StainAI is fast, accurate and adaptable. It uses common laboratory equipment. Its creators, Michael Shoykhet, MD, PhD, at CNH and Dr. Tsang-Wei Tu at HU, are making StainAI available to other researchers. They hope StainAI will help labs worldwide discover new ways to protect children’s brains from inflammation and injury.

You can read the full article, StainAI: quantitative mapping of stained microglia and insights into brain-wide neuroinflammation and therapeutic effects in cardiac arrest, in Communications Biology, a Nature group journal.

Anthony Sandler, MD, at Axios’ inaugural Future of Health Summit

In the news: Axios’ Future of Health Summit

“Healthcare is moving very fast. And what often happens in adults, also happens in children. Unfortunately, most of the research is directed initially at adults, and then whittles down to children. At Children’s National, we’re trying to turn that around. We’re trying to do research for children that will expand its way up to adults, turning it on its head.”

Anthony Sandler, MD, senior vice president and surgeon-in-chief, Joseph E. Robert Jr. Center for Surgical Care, and director of the Sheikh Zayed Institute for Pediatric Surgical Innovation highlighted the exciting research and innovation happening at Children’s National – including demonstrating a technology, led by Raj Shekhar, PhD, that uses real-time imaging with augmented reality to project live ultrasound visualization of a patient within the surgeon’s field of view. This enhances surgical precision and ultimately supports positive patient outcomes.

This conversation was a part of Axios’ inaugural Future of Health Summit – an event bringing together the top voices in healthcare, policy and technology to explore the biggest challenges and innovations shaping the future of medicine.

Doctors from Children's National Hospital in Uganda

Children’s National brings AI into the RHD early diagnosis equation

In December of 2024, a team that included experts from Children’s National Hospital traveled to Uganda to continue work on a pilot program applying artificial intelligence (AI) to the diagnosis of rheumatic heart disease (RHD). Ugandan health care providers have been trained and equipped to acquire echocardiograms for their patients but lack expertise in consistently being able to diagnose RHD by detecting leaky heart valves. The team created a tool that uses AI to predict RHD by identifying leaky heart valves on handheld ultrasound devices, then prompts a referral for a full echocardiogram.

The goal is to find ways to help people in Uganda diagnose RHD early, before a patient is in need of surgery, and initiate antibiotics so their heart can return to normal. The team of researchers, including fellow Kelsey Brown, MD, helped to implement additional steps toward this goal in December. According to Dr. Brown, the results were excellent. After four days of seeing patients, over 450 people were screened. The AI tool has an 86% accuracy rating. After returning from Uganda, the research team plans to work on the AI tool and further improve its accuracy rating. Eventually, the vision is that this tool can roll out on a larger scale for more places around the world to access it.

Craig Sable, MD, Marius Linguraru, DPhil, MA, MSc, and Pooneh Roshanitabrizi, PhD, from our Sheikh Zayed Institute, who developed the AI algorithms, worked in partnership with the Rheumatic Heart Disease Research Collaborative (RRCU) in Uganda. This trip was also made possible thanks to a grant funded through the Children’s National Global Health Initiative. Special thank you to our AI partner, US2.AI, who made the deployment of the AI models onto a tablet that provided real-time results, possible.

Illustration of a brain, stethoscope and computer chip

Global expert consensus defines first framework for building trustworthy AI in health care

Illustration of a brain, stethoscope and computer chip

The guidelines are the first globally acknowledged framework for developing and deploying health care AI applications and gauging whether the information they generate can be trusted or not.

More than 100 international experts in the application of artificial intelligence (AI) in health care published the first set of consensus guidelines that outline criteria for what it means for an AI tool to be considered trustworthy when implemented in health care settings.

The guidelines, published in the journal the BMJ, are the first globally acknowledged framework for developing and deploying health care AI applications and gauging whether the information they generate can be trusted or not.

What this means

Called the FUTURE-AI framework, the consensus guidelines are organized based on six guiding principles:

  • Fairness
  • Universality
  • Traceability
  • Usability
  • Robustness
  • Explainability

The cadre of experts reviewed and agreed upon a set of 30 best practices that fall within the six larger categories. These practices address technical, clinical, socio-ethical and legal aspects of trustworthy AI. The recommendations cover the entire lifecycle of health care AI: design, development and validation, regulation, deployment and monitoring.

The authors encourage researchers and developers to take these recommendations into account in the proof-of-concept phase for AI-driven applications to facilitate future translation to clinical practice.

Why it matters

“Patients, clinicians, health organizations and authorities need to know that information and analysis generated by AI can be trusted, or these tools will never make the leap from theoretical to real world application in a clinical setting,” says Marius George Linguraru, DPhil, MA, MSc, Connor Family Professor for Research and Innovation in the Sheikh Zayed Institute for Surgical Innovation at Children’s National Hospital and co-author of the guidelines. “Bringing so many international and multi-disciplinary perspectives together to outline the characteristics of a trustworthy medical AI application is part of what makes this work unique. It is my hope that finding such broad consensus will shed light on the greater good  AI can bring to clinics and help us avoid problems before they ever impact patients.”

The FUTURE-AI consortium was founded by Karim Lekadir, PhD, ICREA Research Professor at the University of Barcelona in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists and social scientists. Over a 2-year period, the consortium established these guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey and online consensus meetings. Dr. Linguraru contributed with a unique perspective on AI for pediatric care and rare diseases.

What’s next

The authors note that, “progressive development and adoption of medical AI tools will lead to new requirements, challenges and opportunities. For some of the recommendations, no clear standard on how these should be addressed yet exists.”

To tackle this uncertainty, they propose FUTURE-AI as a dynamic, living framework. This includes a dedicated website to allow the global community to participate in the FUTURE-AI network. Visitors can provide feedback based on their own experiences and perspectives. The input gathered will allow the consortium to refine the FUTURE-AI guidelines and learn from other voices.

Read the full manuscript outlining all 30 best practices: FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Marius George Linguraru

Marius George Linguraru appointed as president of the MICCAI Society

Marius George Linguraru

“MICCAI has been a professional home for me throughout my career and I am deeply honored to have a chance to give back to the organization,” said Dr. Linguraru.

We’re pleased to announce that Marius George Linguraru, DPhil, MA, MSc, Connor Family professor and endowed chair in Research and Innovation at Children’s National, has been elected as president of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society board of directors. Dr. Linguraru has been involved in the society since he attended his first MICCAI conference in 2001. He was elected to the board of directors in 2021 and chairs the Career Development Working Group. Dr. Linguraru was instrumental in establishing the MICCAI Mentorship Program, the MICCAI Start-up Village and the AFRICAI Special Interest Group. He also served as the program chair of MICCAI 2024, which received and reviewed a record number of paper submissions. He will begin his three-year term as president on February 1, 2025. Watch Dr. Linguraru’s brief inaugural message to members here.

“MICCAI has been a professional home for me throughout my career and I am deeply honored to have a chance to give back to the organization,” said Dr. Linguraru. “I believe the society is poised not just to meet the challenges of the next few years, but to thrive as an essential leadership forum for the growth of medical image computing, computer assisted intervention and artificial intelligence in healthcare.”

Dr. Linguraru leads the AI research initiatives at Children’s National and serves as principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation. His award-winning team builds artificial intelligence applications to expand health equity and access to pediatric healthcare when diseases are rare and resources are limited. Through partnerships between Children’s National and Virginia Tech and Microsoft, Dr. Linguraru also plays an integral role in exploring how generative AI can improve pediatric care.

Meanwhile, Caroline Essert, PhD, MSc, completes her term as president of the MICCAI Society on January 31, 2025.

“It has been an honor to contribute to the growth and vibrancy of this incredible community,” said Dr. Essert. “I extend my warmest welcome to Dr. Linguraru as the incoming president of the MICCAI Board. I am confident that under his leadership, the MICCAI Society will reach new heights and continue to serve as a beacon of excellence in our field.” Read her full farewell message here.

To learn more about the MICCAI Society, click here.

robotic surgery apparatus

Charging ahead: Researchers develop robotic renal tumor surgery

robotic surgery apparatus

Researchers at Children’s National Hospital are developing supervised autonomous robotic surgery to make expert kidney tumor removal accessible in rural areas, combining robotics, AI and surgeon oversight for safer, more precise outcomes.

Imagine a robot capable of planning and executing the intricate removal of a cancerous kidney tumor — a concept that might sound like science fiction. Yet this groundbreaking work is underway at the Sheikh Zayed Institute (SZI) for Pediatric Surgical Innovation at Children’s National Hospital.

Called Supervised Autonomous Robotic Renal Tumor Surgery (SARRTS), the project aims to prove that a supervised autonomous kidney resection is feasible. Its goal is to enable general surgeons in rural hospitals to oversee robots performing complex resections, democratizing access to specialized surgical care. Backed by a $1 million contract from the Advanced Research Projects Agency for Health (ARPA-H), the initiative represents new opportunities in medical innovation.

“The hope is that, someday, patients will no longer have to travel to major oncology centers to get the best possible surgical outcome when faced with renal tumors,” said Kevin Cleary, PhD, associate director of engineering at SZI. “We hope to combine the precision of robotics with a surgeon’s clinical expertise to create consistently high outcomes.”

The patient benefit

Surgery is a cornerstone of cancer treatment, but access to skilled surgeons remains unevenly distributed nationwide. Autonomous robotic surgery could address this disparity by increasing access to expert-level care, enhancing the precision and consistency of procedures and unlocking new surgical possibilities beyond human surgeons’ capabilities.

Under the initial concept, the SARRTS system will use a combination of CT imaging and 3D mapping from a robot’s RGB-depth camera. While the robot independently plans and executes the incision and tumor resection, the supervising surgeon retains full control, with the ability to approve, modify or halt the procedure at any time — an interplay between human expertise and robotic precision to help ensure safety.

Testing will be conducted on realistic kidney models, called phantoms, which are designed to train and test surgical outcomes. The project aims to validate the feasibility of supervised autonomous tumor resection while advancing technologies that could pave the way for broader applications.

“Robotics and medicine have finally reached a point where we can consider projects requiring this level of complexity,” said Anthony Sandler, MD, senior vice president and surgeon-in-chief at Children’s National and executive director of SZI. By combining autonomous robotics, artificial intelligence and surgical expertise, we can profoundly impact the lives of patients facing life-altering cancer diagnoses.”

Children’s National leads the way

In addition to the kidney surgery initiative, the Children’s National team is pursuing other groundbreaking projects. These include a second ARPA-H contract focused on robotic gallbladder removal and a National Institutes of Health grant to explore robotic hip-pinning, a procedure used to repair fractured hips with pins, screws and plates.

Axel Krieger, PhD, an associate professor of mechanical engineering at Johns Hopkins University, is collaborating closely on the kidney resection and gallbladder projects. The interdisciplinary team believes this state-of-the-art care could be tested and developed within the next decade.

“This particular surgery is complex, and a robot may offer advantages to address difficulties created by patient anatomy and visibility within the surgical field,” said Dr. Sandler. “We can imagine a day – in the not too distant future – when a human and a robotic arm could team up to successfully advance this care.”

This project has been funded in whole with federal funds from ARPA-H under cooperative agreement AY1AX000023.

Children's National Hospital's winning team for the Brain Tumor Segmentation-Africa (BraTS-Africa) challenge

AI for good: Children’s National wins global competitions for measuring brain tumors

Children's National Hospital's winning team for the Brain Tumor Segmentation-Africa (BraTS-Africa) challenge

Meet the winners (left to right): Syed M. Anwar, Ph.D., M.S., principal investigator at Children’s National; Daniel Capellan Martin, M.Sc., Polytechnic University of Madrid; Abhijeet Parida, data scientist at Children’s National; and Austin Tapp, Ph.D., postdoctoral research fellow at Children’s National.

Using an award-winning artificial intelligence (AI) algorithm developed at Children’s National Hospital, researchers ranked first in the world in the Brain Tumor Segmentation-Africa (BraTS-Africa) challenge for their approach to identifying different parts of deadly gliomas. The details of their innovative method were recently published on arXiv, a curated research-sharing platform.

“Technology can bridge the gap in healthcare between high- and low-resource countries,” 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 (SZI). “By tailoring methods we created at our hospital to fit the needs of specific regions, such as sub-Saharan Africa, our research helps improve medical imaging and diagnosis in challenging environments.”

Dr. Linguraru was the program chair of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2024 in Marrakesh, Morocco, the leading global meeting on AI in medical imaging.

Children’s National leads the way

Gliomas are a type of brain tumor with a high death rate and are especially difficult to diagnose in low- and middle-income countries. Given the increased need in Africa, researchers worldwide came together in Morocco to compete over the best way to accurately detect and measure tumors using MRI data and AI.

By applying advanced machine-learning techniques, the researchers adapted tools initially designed for well-resourced settings to work in countries with far fewer.

The study focused on transfer learning, a process in which an AI model is trained in advance on a large number of brain tumor images and then adjusted to work with smaller sets of new data. In this case, the models were adapted to work with local sub-Saharan African data using a strategy to combine different models’ strengths.

When tested, the approach achieved impressive accuracy scores. The Children’s National team, which included a colleague from the Polytechnic University of Madrid, ranked first in the BraTS-Africa 2024 challenge for identifying different parts of gliomas.

“To make the method widely available, the winning algorithm is shared online for others to use and improve upon,” Dr. Linguraru said. “My favorite part of these competitions is how they highlight the way innovation and collaboration can reduce global healthcare inequalities.”

The big picture

Children’s National researchers consistently lead global events using AI and advanced imaging to tackle complex healthcare challenges. In 2023, the team won a global contest to measure pediatric brain tumors at the MICCAI 2023 Conference. This year’s success in the BraTS-Africa challenge builds on this knowledge base and expands its use to adult gliomas.

At the Radiological Society of North America 2024 annual meeting, which drew 50,000 attendees, Zhifan Jiang, Ph.D., a staff scientist in the Precision Medical Imaging Lab at SZI, also won the Cum Laude Award for his scientific poster on applying AI to radiological images to predict severe outcomes for children with brain tumors caused by neurofibromatosis type 1.

“These achievements show how our science is leading the world in using AI for good,” Dr. Linguraru said. “Every day, we’re building on our knowledge of advanced imaging, brain tumors and AI to improve the diagnosis, measurement and treatment of deadly tumors — on a global scale.”

Attendees of the Brain Tumor Segmentation-Africa (BraTS-Africa) challenge

illustration of bacterial outbreak

$2.2m grant to support AI-driven sepsis program for pediatric care

illustration of bacterial outbreak

Sepsis is a rapidly progressing, life-threatening condition characterized by organ dysfunction (OD) resulting from an immune response to infection.

Researchers at Children’s National Hospital are investigating pediatric sepsis, a leading cause of in-hospital death, particularly in underserved and minoritized populations.

A new Maximizing Investigators’ Research Award (MIRA) (R35) is granting over $2.2 million to Children’s National to establish a comprehensive sepsis program that will include a diagnostic artificial intelligence (AI) based, biomarker-enhanced platform for early recognition of pediatric sepsis and cutting-edge, extracellular vesicle-based therapeutics that can significantly decrease mortality and long-term disease sequelae.

MIRA provides support for research in an investigator’s laboratory that falls within the mission of The National Institute of General Medical Sciences.

Why it matters

Sepsis, defined as a rapidly progressive, is a life-threatening organ dysfunction (OD) due to an immune dysregulation as a response to infection. In the U.S., it results in over 75,000 pediatric admissions on an annual basis with an associated mortality rate of 5 to 20%.

“Sepsis has been linked to 20% of deaths worldwide and despite some recent advances in diagnostic tools, the lack of accurate definitions and heterogeneity of this clinical syndrome have led to delays in recognition and treatment with deleterious effects on the health of millions of children, especially those from minority groups,” says Ioannis Koutroulis, M.D., Ph.D., M.B.A., research director of Emergency Medicine at Children’s National. “Additionally, current sepsis therapeutic regimens are mostly supportive, lack the necessary personalization and fail to address the underlying physiological processes leading to sepsis-induced life threating organ failure.”

How does it move the field forward

High mortality rates and no significant new treatments in recent years due to sepsis presents a critical opportunity to make a global impact. Advancements in early recognition and intervention could save millions of lives. By utilizing AI and biomarkers for the early detection of sepsis in children, medical professionals could have the potential to greatly improve outcomes by enabling timely treatment.

“There is an urgent need to focus more attention on this condition and develop effective solutions to combat its devastating effects,” Dr. Koutroulis adds.

How we’re leading the way

The high-volume pediatric emergency room will serve as a crucial site for recruiting patients. With access to state-of-the-art laboratories, the research will be conducted in facilities equipped with cutting-edge technology, ensuring accurate and efficient analysis. This combination of a high patient volume and advanced research infrastructure will enable the program to deliver reliable results and make significant strides in the fight against pediatric sepsis.

“This grant will allow for early recognition of pediatric sepsis and treatment with an innovative approach using extracellular vesicle-based therapies that can directly affect immune and metabolic processes,” Dr. Koutroulis said.

2024 with a lightbulb instead of a zero

The best of 2024 from Innovation District

2024 with a lightbulb instead of a zero2024 marked another groundbreaking year for Children’s National Hospital, showcasing remarkable advances across the spectrum of pediatric medicine, research and healthcare innovation. From pioneering surgical procedures to breakthrough artificial intelligence applications, the institution continued to push the boundaries of what’s possible in children’s healthcare. Read on for our list of the most popular articles we published on Innovation District in 2024.

1. Prenatal COVID exposure associated with changes in newborn brain

A study led by researchers at Children’s National Hospital showed that babies born during the COVID-19 pandemic have differences in the size of certain structures in the brain, compared to infants born before the pandemic. The findings suggest that exposure to the coronavirus and being pregnant during the pandemic could play a role in shaping infant brain development.
(3 min. read)

2. Children’s National Hospital again ranked among the best in the nation by U.S. News & World Report

Children’s National Hospital was ranked as a top hospital in the nation by the U.S. News & World Report 2024-25 Best Children’s Hospitals annual rankings. This marks the eighth straight year Children’s National has made the Honor Roll list. The Honor Roll is a distinction awarded to only 10 children’s hospitals nationwide.
(2 min. read)

3. Children’s National performs first ever HIFU procedure on patient with cerebral palsy

In January 2023, a team of multidisciplinary doctors performed the first case in the world of using bilateral high intensity focused ultrasound (HIFU) pallidotomy on Jesus, a 22-year-old patient with dyskinetic cerebral palsy. The procedure is part of a clinical trial led by Chima Oluigbo, M.D., pediatric neurosurgeon at Children’s National Hospital.
(3 min. read)

4. Novel ultrasound device gets FDA breakthrough designation with Children’s National support

A novel ultrasound device developed by Bloom Standard received the Food and Drug Administration’s valued breakthrough device designation with the help of Children’s National Hospital. The device that enables autonomous, hands-free ultrasound scans to be performed anywhere, by any user.
(2 min. read)

5. First-of-its-kind pilot study on the impacts of Lyme disease in pregnancy and infant development

Understanding the effects of Lyme disease on the developing fetal brain is essential to ensure timely prenatal and postnatal treatments to protect the fetus and newborn. In response to this need, Children’s National Hospital is leading a pilot study to establish the groundwork needed for a larger study to determine the effect of in utero exposure to Lyme disease on pregnancy and early childhood neurodevelopmental outcomes.
(3 min. read)

6. Earliest hybrid HLHS heart surgery kids thrive 5 years later

Five years ago, Cayden was born 6 weeks early weighing less than four pounds and at risk of dying from her critical congenital heart disease. Today, she’s a happy five-year-old. Early diagnosis of her hypoplastic right ventricle, double inlet left ventricle and critical coarctation of the aorta allowed for the team at Children’s National Hospital to create a careful plan for safe delivery and to offer an innovative hybrid HLHS surgical approach at the hospital within 24 hours after she was born.
(1 min. read)

7. Wayne J. Franklin, M.D., F.A.C.C., named senior vice president of the Children’s National Heart Center

Children’s National Hospital appointed Wayne J. Franklin, M.D., F.A.C.C., as the new senior vice president (SVP) of the Children’s National Heart Center. In this role, Dr. Franklin oversees the full spectrum of heart care services including cardiac imaging and diagnostics, interventional cardiology, electrophysiology, cardiac anesthesia, cardiac surgery and cardiac intensive care.
(2 min. read)

8. Artificial – and accelerated – intelligence: endless applications to expand health equity

By pioneering artificial intelligence (AI) innovation programs at Children’s National Hospital, Marius George Linguraru, D.Phil., M.A., M.Sc., and the AI experts he leads are ensuring patients and families benefit from a coming wave of technological advances. The team is teaching AI to interpret complex data that could otherwise overwhelm clinicians.
(4 min. read)

9. Evidence review: Maternal mental conditions drive climbing death rate in U.S.

Painting a sobering picture, a research team led by Children’s National Hospital culled years of data demonstrating that maternal mental illness is an under-recognized contributor to the death of new mothers. They called for urgent action to address this public health crisis.
(3 min. read)

10. Nathan Kuppermann, M.D., M.P.H., named chief academic officer and chair of Pediatrics

Children’s National Hospital appointed Nathan Kuppermann, M.D., M.P.H., as its new executive vice president, chief academic officer and chair of Pediatrics. In this role, Dr. Kuppermann oversees research, education and innovation for the Children’s National Research Institute as well as academic and administrative leadership in the Department of Pediatrics at George Washington University School of Medicine & Health Services.
(2 min. read)

11. First global clinical trial achieves promising results for hypochondroplasia

Researchers from Children’s National Hospital presented findings from the first clinical trial of the medication vosoritide for children with hypochondroplasia – a rare genetic growth disorder. During the phase 2 trial, researchers found vosoritide increased the growth rate in children with hypochondroplasia, allowing them to grow on average an extra 1.8 cm per year.
(2 min. read)

12. Pioneering research center aims to revolutionize prenatal and neonatal health

Since its establishment in July 2023, the Center for Prenatal, Neonatal & Maternal Health Research at Children’s National Hospital has gained recognition through high-impact scientific publications, featuring noteworthy studies exploring the early phases of human development.
(3 min. read)

Alda Mizaku

Meet Children’s National’s new Chief Data and Artificial Intelligence Officer

Alda Mizaku

In June, Alda Mizaku, M.S., became the hospital’s first chief data and AI officer.

Artificial intelligence (AI) is revolutionizing healthcare and will shape the future of pediatrics. It can drive efficiency, supercharge research and improve patient outcomes. Harnessing AI safely and ethically takes thoughtful leadership. In June, Alda Mizaku, M.S., became the hospital’s first chief data and AI officer. Previously, she led data engineering and analytics at Mercy Health in St. Louis for 11 years. We asked Mizaku about her work and vision for Children’s National.

Q: What excites you about your new role?

A: I am passionate about the opportunity to leverage technology to create better experiences for children and families. Embracing innovation can help us create more health equity for our community. This is an exciting time in healthcare. I’m committed to leading the way with compassionate and cutting-edge solutions. This includes realizing the full potential of medical data from electronic healthcare records and other sources. AI can help us develop treatment plans tailored specifically for each child. It also can make proactive recommendations to coordinate patient care.

Q: What have you seen AI accomplish in medicine and how do you envision its growth and impact in pediatrics?

A: AI yields helpful insights to understand each patient’s individual needs. It takes complex medical data and makes it more useful. It can help us diagnose disease and improve care coordination for each patient family. AI fits very well into pediatrics because children’s hospitals put a lot of effort into research and development. For example, in rare disease, there’s an emphasis on building models to understand a condition’s genetic composition. AI gives us the opportunity to find solutions and intervene more quickly to change lives. This is the future of pediatric medicine.

Q: How will children’s national use AI to improve patient care?

A: We have been busy creating an enterprise cloud data platform. It will allow us to bring all of the hospital’s data into one place and create one true source of information.
This one-stop shop will make it much easier for our researchers and care providers to access the information they need to make a difference for patients. AI will help with operational efficiencies. It will give us a clearer picture of which units across the institution are busy or have extra capacity. It can recommend ways to eliminate bottlenecks. This reduces wait times and allows us to help more patients.

Q: How can AI help our faculty?

A: The beauty of AI is that it can help faculty focus more on patient care and less on their administrative tasks.
Jessica Herstek, M.D., our chief medical informatics officer, is leading our pilot of an AI-based ambient listening technology that creates notes during patient encounters. The clinician focuses on their patient. Later, they can refine and approve the notes.

Accountability remains important. Just because we’re leveraging technology, it doesn’t remove accountability for staff.
AI assists providers and reinforces their role in care. Medical innovations that leverage AI also can increase their efficiency.
For example, liquid biopsy technologies use AI to study blood samples and detect cancer. This helps patients avoid time-consuming scans and painful traditional biopsies. We can detect disease or its recurrence much earlier in a less invasive way. This enhances care.

Q: What are some challenges we face on the road to implementing AI?

A: Embracing AI systems may involve giving up some comfort in the way that we’ve always done things. It opens up possibilities, but it requires some change. Our challenge is to make sure we have three things in place to create scalable, sustainable solutions. The first is having high-quality, integrated data. The second is collaboration. The third is change management.

We will take an inclusive approach to implementing changes, working side by side with clinical and operational leaders. When we present solutions, it will be collaborative. Comprehensive training also plays a key role. We must address misconceptions about AI’s capabilities and foster a common understanding of its most effective uses.
Our recipe for success will be openness to contributing to better outcomes for our patients.
We need to collect high-quality data consistently across different units. Variations don’t translate well to scalable solutions crucial to generative AI. When we look at the big picture, it’s clear we can come together to provide the best care.

Q: Why is this work important to you?

A: Technology and its capacity to transform lives has always captivated me. Growing up in Albania, my dad led the pharmacy at the local hospital.
Sometimes I would ride in the ambulance when he needed to go to the hospital urgently. I was around 7 years old at the time, and it left a deep impression.
I recognized that each member of the team played a significant role in caring for the patient. This experience inspires my work to this day.

Read more stories like this one in the latest issue of Believe magazine.

Doctor using digital tablet for advanced Mri x-ray scan

AI’s transformative potential in radiology

Doctor using digital tablet for advanced Mri x-ray scan

The adoption of artificial intelligence (AI) has the potential to enhance radiological imaging, improve diagnostic capabilities and reduce burnout in the field.

The adoption of artificial intelligence (AI) has the potential to enhance radiological imaging, improve diagnostic capabilities and reduce burnout in the field, provided that physicians and scientists work together to ensure its careful integration into the practice of medicine, according to a special report in Radiology: Artificial Intelligence, a journal of the Radiological Society of North America (RSNA).

Assembled by experts in radiology, medical imaging and machine learning, the special report lays out the clinical, cultural, computation and regulatory considerations that are being introduced, particularly as generative AI models become part of the field.

“AI tools can play a key role in radiology, but radiologists must be able to trust in the systems’ design and receive adequate training. As the physicians most familiar with these tools, radiologists should establish clear guidelines regarding clinical accountability,” 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.

Moving the field forward

Dr. Linguraru and his peers assembled the report based on a series of seminars hosted by RSNA and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society. They collected input from multidisciplinary experts to outline the current clinical uses of AI and its future potential.

The experts agreed that collaboration between radiologists and AI scientists will be essential to successfully integrate AI into the discipline of radiology. This partnership should focus on establishing a unified agenda, shared language and clear expectations of the tools developed. By working together, they can ensure that AI tools are designed and implemented to meet the practical needs of radiology, particularly with the incorporation of language and vision models.

What’s next

Among the challenges ahead, clinical institutions must align their staffing, data management and computational resources to deploy and monitor AI systems effectively. This alignment includes ensuring that personnel are adequately trained to use AI tools, that data is managed and processed efficiently and that sufficient computational power is available to support AI operations. Cloud computing may be vital to hospitals that don’t have hardware and technical maintenance resources.

“The successful integration of AI in radiology depends on trust in AI design, collaborative efforts between radiologists and AI scientists, and the alignment of clinical resources to support AI deployment,” Dr. Linguraru said. “With these factors in place, AI can play a transformative role in improving radiological practices and outcomes.”

Read the special report “Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts” in Radiology: Artificial Intelligence.

Dr. Craig Sable in Uganda

Around the world

Our Global Health Initiative launched in 2016 with the goal of eliminating pediatric health disparities around the world. We aim to address the most pressing pediatric health issues through better care for medically underserved populations. This leadership helps us achieve our mission of caring for all children. A broad range of education and research projects improves health outcomes. They also offer enriching opportunities for experienced faculty and emerging leaders to advance clinical excellence.

Healing hearts in Uganda

Dr. Craig Sable in Uganda

Dr. Craig Sable and team train partners in Uganda.

Craig Sable, M.D., interim chief of Cardiology, improves care for young people with rheumatic heart disease (RHD) in Uganda. Donors, including the Karp Family Foundation, Huron Philanthropies, Zachary Blumenfeld Fund and the Wood family, make this possible. RHD affects 50 million people, mostly children, worldwide. It claims 400,000 lives each year.

Dr. Sable and Ugandan partners completed important research showing that early RHD detection, coupled with monthly penicillin treatment, can protect the heart. They are working on practical solutions, such as a new portable device with artificial intelligence (AI) that can easily screen for RHD.

In 2023, Dr. Sable led two missions in Uganda where he and his team did surgeries and special tests for 18 children with RHD. They also taught local doctors new skills to help more kids on their own.

Plastic surgery and reconstructive care in Kenya and Nepal

Each year our Craniofacial & Pediatric Plastic Surgery team, under the leadership of Johnston Family Professor of Pediatric Plastic Surgery and Chief of Pediatric Plastic Surgery Gary Rogers, M.D., J.D., LL.M., M.B.A., M.P.H., provides opportunities for fellows to participate in surgical missions.

In 2024, Perry Bradford, M.D., traveled to the Moi Teaching Hospital in Eldoret, Kenya where she provided patients with burn, pressure wound and cleft reconstruction. She built community connections with the local plastic surgeons and educated registrars and medical students. “This gave me firsthand experience working in a community with limited resources and forced me to be more creative,” Dr. Bradford says. “The experience inspired me to examine what it means to have consistent access to advanced tools and equipment.”

In 2022, a group traveled to Nepal to provide care. Some patients arrived after days of travel by yak or buffalo. One child with a burn injury recovered use of her hand. The team educated local providers to deliver life-changing treatments unavailable in Nepal.

Dr. Tesfaye Zelleke in Ethiopia

Dr. Tesfaye Zelleke, left, and team in Ethopia.

Elevating epilepsy care in Ethiopia

Neurologist Tesfaye Zelleke, M.D., and partners in Ethiopia are seeking to improve the lives of children with epilepsy. The BAND Foundation provides support. Ethiopia has a population of about 120 million yet only a handful of pediatric neurologists.

Dr. Zelleke’s team trained nonspecialist providers to diagnose and treat children in the primary care setting. They also launched a mobile epilepsy clinic to provide community care and build the capacity of local clinicians. In collaboration with advocacy groups, the team educates the public about epilepsy with a goal of reducing stigma.

New hope in Norway

In 2023, our Division of Colorectal & Pelvic Reconstruction shared its expertise with clinicians at Oslo University Hospital, Rikshospitalet, in Norway. This effort was a key first step in Oslo becoming the first dedicated colorectal center in Scandinavia.

Marc Levitt, M.D., and team members performed complex surgeries otherwise unavailable for waiting patients. They led an academic conference. They held clinics to educate nurses, reviewed patient records and made care recommendations. Specialized care enabled a young patient with significant bowel difficulties to recover function and lead a normal life.

The team will travel to South Africa, the Czech Republic and Spain in 2024. Donors, including The Dune Road Foundation and Deanna and Howard Bayless, make this work possible.

Improving outcomes for babies in the Congo

AI can be a valuable tool for diagnosing genetic conditions. It detects unique facial patterns that clinicians without genetics training can miss. However, existing facial analysis software struggles in nonwhite populations.

A team led by Marius George Linguraru, D.Phil., M.A., M.Sc., the Connor Family Professor of Research and Innovation and principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation, is working to improve the newborn diagnosis rate worldwide. They are testing smartphone software in the Democratic Republic of Congo. Diverse newborn data improves AI’s ability to detect a variety of genetic conditions in more children. Early detection, diagnosis and informed care lead to better health outcomes.

Nephrology care for kids in Jamaica

Dr. Moxey-Mims and team in Jamaica

Jennifer Carver and Dr. Marva Moxey-Mims, center, with staff at Bustamante Children’s Hospital.

Marva Moxey-Mims, M.D., chief of Nephrology, is bringing care to children with kidney disease in Jamaica, with a goal of improving health equity. An International Pediatric Nephrology Association grant helped make it possible.

On a recent trip, Dr. Moxey-Mims and a small team — including Jennifer Carver, RN, CNN, lead peritoneal dialysis nurse at Children’s National, and three pediatric nephrologists from Jamaica — trained nearly 30 nurses from Jamaican hospitals. Nurses received hands-on dialysis education to improve their clinical skills. The team also worked to educate the community in disease awareness and prevention.

Read more stories like this one in the latest issue of Believe magazine.

person using a ChatBot

Study finds difficulty distinguishing between human and AI-written abstracts

person using a ChatBotA new study published in JAMA Pediatrics suggests healthcare professionals struggle to identify research abstracts written by artificial intelligence (AI) compared to those written by humans.

The study, led by Dennis Ren, M.D., emergency medicine provider at Children’s National Hospital, highlights the lack of established standards for the use of AI in scientific writing and publishing.

The big picture

The researchers presented 102 healthcare professionals with four research abstracts: two written by human researchers from the Pediatric Academic Societies Meeting in 2020, and two generated by ChatGPT 3.5 (OpenAI). The participants were asked to identify the abstracts’ origin and state how they made their determination.

The participants were able to identify the abstracts’ origins correctly 43% of the time, but accuracy ranged from 20% to 57%. This suggests that healthcare professionals cannot reliably distinguish between research abstracts written by humans and those written by AI. Interestingly, 72.5% of participants believed using AI for research abstracts was ethical.

Why it matters

AI tools are becoming more widely used in scientific research, including writing and editing scientific content. However, there are no set standards for what constitutes the appropriate use of AI in scientific writing and publishing. This study asks the critical question: can healthcare professionals even tell the difference between AI and human generated content?

In conclusion, the authors state that they have no reservations about using AI to generate abstracts or even full articles as long as the final product can be reviewed and edited.

“AI may help with knowledge dissemination, but it can also be a source of misinformation and disinformation,” says Dr. Ren. “We need to teach skills of critical thinking and critical appraisal to everyone.”

What’s next

The team is currently exploring other potential applications of AI, such as whether it may be useful in emergency department triage.

Read the full study, Identification of Human-Generated vs AI-Generated Research Abstracts by Health Care Professionals, in JAMA Pediatrics.

Ian Leibowitz

In the News: Advancing innovations in pediatric gastroenterology and hepatology

“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.

healthcare workers putting on PPE

“Mask up!” Soon, AI may be prompting healthcare workers

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.”

Marius Linguraru, D.Phil., M.A., M.Sc., a co-principal investigator for the project, presents

Children’s National joins team to use AI to expand health knowledge in Kenya

Marius Linguraru, D.Phil., M.A., M.Sc., a co-principal investigator for the project, presentsChildren’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.”

lung ct scan

With COVID-19, artificial intelligence performs well to study diseased lungs

lung ct scan

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.”

echocardiogram

AI may revolutionize rheumatic heart disease early diagnosis

echocardiogram

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.

The first outcomes from the program were showcased in a presentation by Dr. Roshanitabrizi at one of the biggest and most prestigious medical imaging and AI computing meetings — the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).

parathyroid close-up

A new imaging device with AI may reduce complications during thyroid surgery

parathyroid close-upSurgeons perform approximately 150,000 thyroidectomies in the United States. Post-surgical complications from this procedure frequently occur due to the misidentification or accidental removal of healthy parathyroid glands. On average, 27% of these patients suffer from transient or permanent hypocalcemia, a condition in which the blood has too little calcium, leading to lifelong complications and socioeconomic burden.

To improve parathyroid detection during surgery, Children’s National Hospital experts developed a prototype equipped with a dual-sensor imaging device and a deep learning algorithm that accurately detects parathyroids, according to a new study published in the Journal of Biophotonics.

“What excited us in this study was that even deep-seated tissues were able to be imaged without light loss, and high resolution imaging was possible due to the unique optical design,” said Richard Jaepyeong Cha, Ph.D., council member of the International Society of Innovative Technologies for Endocrine Surgery and principal investigator for the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital. “Moreover, in several cases, parathyroid autofluorescence was detected even before the surgeon dissected the parathyroid gland, and while it was covered by fat and/or fascia.”

What’s unique

This is the first study that uses color RGB/NIR paired imaging-based parathyroid detection by incorporating multi-modal (both RGB light and near-infrared autofluorescence, or NIRAF, ground truth imaging) data into parathyroid identification using a deep learning algorithm.

The patient benefit

“We envision that our technology will open a new door for the digital imaging paradigm of dye-free, temporally unlimited, and precise parathyroid detection and preservation,” said Richard. “Successful translation of this technology will potentially reduce the risk of hypoparathyroidism after common thyroid surgery and improve the clinical outcomes.”

The results support the effectiveness of their novel approach despite the small sample size, which can potentially improve specificity in the identification of parathyroid glands during parathyroid and thyroid surgeries.

The hold-up in the field

It is often difficult for surgeons with naked eyes to identify parathyroid glands from thyroid tissue because of the small size, the variable position, and similar appearance to the surrounding tissues.

Since 2011, surgeons have benefited from using NIRAF, a non-invasive optical method for intraoperative real-time localization of parathyroids.

While the NIRAF technology has gained traction among endocrine surgery community, false negatives can occur with current devices that use the NIRAF technology in secondary hyperparathyroidism cases. According to Kim et al., the technology is still suboptimal, and a significant percentage of parathyroid is being missed.

Children’s National Hospital leads the way

Engineers in Children’s National are leading this field through several innovations:

  • Non-dye injected, label-free use in real-time in comparison to temporally limited ICG angiography. This technology was featured as the cover article in the journal Lasers in Surgery and Medicine 54(3), 2022.).
  • Simultaneous perfusion assessment from four glands at any time during operation.
  • Arterial flow detection from pulsatile information in well-perfused PG vasculature.
  • Quantified parathyroid detection and classification with prediction values using deep learning technique.

You can read the full study “A co-axial excitation, dual-RGB/NIR paired imaging system toward computer-aided detection (CAD) of parathyroid glands in situ and ex vivo” in the Journal of Biophotonics.

overview of parathyroid surgery procedure

binary numbers

New datasets predict surgeon performance during complications

binary numbers

In a new study published in JAMA Network Open, experts at Children’s National and allied institutions created and validated the first dataset to depict hemorrhage control for machine learning applications, the simulated outcomes following carotid artery laceration (SOCAL) video dataset.

Computer algorithms, such as machine learning and computer vision, are increasingly able to discover patterns in visual data, powering exciting new technologies worldwide. At Children’s National Hospital, physician-scientists develop and apply these advanced algorithms to make surgery safer by studying surgical video.

The big picture

In a new study published in JAMA Network Open, experts at Children’s National and allied institutions created and validated the first dataset to depict hemorrhage control for machine learning applications, the simulated outcomes following carotid artery laceration (SOCAL) video dataset.

The authors designed SOCAL to serve as a benchmark for data-science applications, including object detection, performance metric development and outcome prediction. Hemorrhage control is a high-stakes adverse event that can pose unique challenges for video analysis. With SOCAL, the authors aim to solve a valuable use case with algorithms.

“As neurosurgeons, we are often called to perform high-risk and high-impact procedures. No one is more passionate about making surgery safer,” said Daniel Donoho, M.D., neurosurgeon at Children’s National Hospital and senior author of the study. “Our team at Children’s National and the Sheikh Zayed Institute is poised to lead this exciting new field of surgical data science.”

The hold-up in the field

These algorithms require raw data for their development, but the field lacks datasets that depict surgeons managing complications.

By creating automated insights from surgical video, these tools may one day improve patient care by detecting complications before patients are harmed, facilitating surgeon development.

Why it matters

“Until very recently, surgeons have not known what may be possible with large quantities of surgical video captured each day in the operating room,” said Gabriel Zada, M.D., M.S., F.A.A.N.S., F.A.C.S., director of the Brain Tumor Center at the University of Southern California (USC) and co-author of the study. “Our team’s research led by Dr. Donoho shows the feasibility and the potential of computer vision analysis in surgical skill assessment, virtual coaching and simulation training of surgeons.”

The lack of videos of adverse events creates a dataset bias which hampers surgical data science. SOCAL was designed to meet this need. After creating a cadaveric simulator of internal carotid artery injury and training hundreds of surgeons on the model at nationwide courses, the authors then developed computational models to measure and improve performance.

“We are currently comparing our algorithms to experts, including those developed using the SOCAL dataset,” Dr. Donoho said. “Human versus machine, and our patients are ultimately the winners in the competition.”

What’s next

The authors are also building a nationwide collective of surgeons and data scientists to share data and improve algorithm performance through exciting partnerships with USC, California Institute of Technology and other institutions.

You can read the full study “Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications” in JAMA Network Open.