Tag Archive for: Marius Linguraru

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.

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

A speaker at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health

Transforming pediatric care: How AI is driving the next medical revolution

  • Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health attendees.
  • Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health attendees.
  • A speaker at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health
  • A speaker at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health
  • A speaker at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health
  • A speaker at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health
  • A speaker at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health
  • A speaker at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health
  • A speaker at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health
  • Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health attendees.
  • Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health attendees.
  • A panel at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health
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The future of healthcare is unfolding before scientists and clinicians: Doctors are assisted by virtual scribes trained by artificial intelligence. Algorithms are reading MRIs. Smartphones are helping to detect strep throat. Machines diagnose children without access to care.

These and dozens of other artificial intelligence (AI) applications are being tested to enhance pediatric healthcare, and many were on display at the 2nd annual Children’s National Hospital-Virginia Tech Symposium on AI for Pediatric Health at the Children’s National Research & Innovation Campus.

Some highlights from the daylong conversation about the future of pediatric medicine, augmented by AI and generative AI models capable of producing new and critical content:

  • 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: “Children are just not mini-adults. In pediatric care, we train pediatric specialists because kids die from different diseases than those that kill adults. Children also suffer from very impactful and rare conditions. If we train pediatric specialists well, we have to train AI algorithms in the same fashion.”
  • Rowland Illing, M.D., Ph.D., chief medical officer and director of global healthcare and nonprofits at Amazon Web Services: “In a short period of time, the complexity of the models available is astounding. Generative AI, just like AI, can impact outcomes at every step of the patient pathway, including the clinical workflow, care management and patient engagement. By creating a specific use case with generative AI, every step can be optimized to be smarter, which ultimately leads to improved patient care and outcomes.”
  • Children’s National Chief Academic Officer Nathan Kuppermann, M.D., M.P.H.: “AI in pediatric health is not just about identifying rare diseases. Its potential includes all aspects of clinical care, clinical operations, education and research. It has the potential to help educators enhance the novelty and impact of their methods and advance research with powerful tools to gather and analyze data.”
  • Alda Mizaku, vice president and chief data and artificial intelligence officer at Children’s National: “What excites me most about our future is the endless possibilities. We can use AI and data to uncover many things: rare diseases, operational efficiencies, time-saving and cost-saving solutions. This has to be done in a responsible way, and we must look at what some of the guardrails need to be.”

Throughout the day, expert panels offered insights into regulatory pathways to deploy AI in pediatric drugs and devices. The Food and Drug Administration’s Office of Science and Engineering Laboratories also provided guidance on collaborative tools for improving the representation of children and perinatal patients in AI-powered medical devices.

Moving the field forward

Early adopters of AI at Children’s National shared applications already under investigation, including efforts to segment and measure brain tumors on imaging, weigh the risk of strep throat with a smartphone camera and detect rheumatic heart disease with portable technology and an algorithm.

Dr. Linguraru, an expert in healthcare AI, said that artificial intelligence is no longer a hypothetical technology but is already remaking the healthcare system. “AI is here. What matters now is how we use it and how we train doctors to use it well,” he said.

The big picture

Through growing partnerships, Children’s National experts are teaming up with researchers at Virginia Tech on a series of AI-driven projects aimed at advancing pediatric health, including programs to rethink privacy in federated learning, forecast emergency department surges, extract clinical variables from documents to predict sepsis risks, identify rare genetic syndromes in children, and predict single-cell responses to genetic perturbations in pediatric developmental disorders.

Naren Ramakrishnan, Ph.D., director of the Sanghani Center at Virginia Tech and the Thomas L. Phillips Professor in the College of Engineering, said the partnership between the two academic centers is changing healthcare already and will continue to as the organizations offer future seed grants to support innovation in cardiology, neuroscience and oncology. “The roots have borne fruit,” he said.

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.”

Hyperfine Swoop System

$1.6m grant to boost MRI access globally for maternal, child health

Researchers at Children’s National Hospital are investigating ways to bring more portable and accessible low-field magnetic resonance imaging (MRI) to parts of the world that lack access to this critical diagnostic tool, thanks to a grant from the Bill & Melinda Gates Foundation.

The nearly $1.6 million in funding will enable clinicians to better treat pediatric neurological conditions including ischemic brain injury, hydrocephalus, micro- and macrocephaly and more, using analysis tools that are designed to handle the loss in image quality and related challenges inherent to low-field MRI. The research brings together teams at Children’s National and Children’s Hospital Los Angeles — two organizations with extensive experience in designing processing software tools for pediatric brain MRI analysis and data enhancement.

The patient benefit

“For 30 years, MRI has primarily helped patients in high-income countries. Our team is thrilled by the prospect of expanding this powerful tool to patients coming from a wide range of nations, geographies and socioeconomic backgrounds,” said Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator at the Sheikh Zayed Institute for Pediatric Surgical Innovation (SZI). “Low-field MRI comes with great advantages including portability at the point of care of patients, lower clinical costs and the elimination of sedation for young children.”

Linguraru and his long-time collaborator, Natasha Lepore, Ph.D., principal investigator at The Saban Research Institute at Children’s Hospital Los Angeles, will analyze data from the brains of children from birth for the maternal and child health studies. The MRI data analyzed will form the basis for future studies of children’s brain anatomy in health and disease.

The big picture

Through the new grant, researchers will develop a suite of tools to help clinicians better analyze data and images from low-field MRI systems. These systems already have been integrated into interventional and observational studies to help characterize early neurodevelopmental patterns and identify drivers of abnormal development. They are also evaluating the efficacy of maternal and infant-focused interventions aimed at improving neurodevelopmental outcomes.

Why we’re excited

At Children’s National, SZI has installed a Hyperfine Swoop system, and Linguraru’s team is creating image enhancement tools tailored to the unique challenges of low-field MRI. Chief among them, conventional processing tools developed over the past several decades remain incompatible with the low-field data and require new software to take full advantage of the diagnostic power of imaging.

The work brings together a prestigious international consortium of scientists and clinicians from around the world to harness the power of computing and expand the reach of diagnostic imaging. Lepore said the team is eager to bring modern medical imaging to parts of the world that have missed its many benefits.

“Children’s brain development in underserved areas can be affected by so many factors, like malnutrition or anemia,” Lepore said. “The software we will design for the Hyperfine scanners will improve research into these factors, so the optimal interventions can be designed. We are excited to bring our expertise to this important and timely project.”

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).

Coronavirus and lungs with world map in the background

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

Coronavirus and lungs with world map in the background

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

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

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

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

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

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

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

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

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

DNA Molecule

Decoding cellular signals linked to hypospadias

DNA Molecule

“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.