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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.”

Research & Innovation Campus

Children’s National pain expert and innovator shares global summit spotlight

Research & Innovation Campus

As a Johnson & Johnson Innovation Quickfire Children’s Challenge awardee, Dr. Finkel and AlgometRx will be among the first group of startups taking up residence at the new JLABS @ Washington, DC, located on the Children’s National Research & Innovation Campus, when it opens in 2021 at the historic former Walter Reed Army Medical Center site.

Medical technology innovator Julia Finkel, M.D., principal investigator for the Pain Medicine Initiative of the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital, recently participated in Galen Growth’s 2020 Global Healthtech Summit on a virtual panel featuring resident companies from Johnson & Johnson Innovation – JLABS who are utilizing artificial intelligence (AI) with the aim to create advanced solutions for diagnostics, treatment and clinical trials. The summit, hosted in Singapore, brought the innovators together to discuss their views on their progress, the challenges and opportunities for bringing medtech innovations to market in the current climate, as well as the tools needed to succeed.

Dr. Finkel’s innovation, AlgometRx, is a real-time pain measurement technology that captures a digital image of a patient’s pupillary response to a non-invasive stimulus and applies proprietary algorithms to measure pain type and intensity. AlgometRx, a spin-off of Children’s National, recently received a JLABS @Washington DC Quickfire Children’s Challenge award.

Joining Dr. Finkel on the panel were JLABS resident company leaders Don Crawford, CEO, Analytics 4 Life; Jim Havelka, CEO, Inform AI; and Kim Walpole, CEO, Trials.ai, which leverages AI to help research teams design more effective clinical trials. The 50-minute program, moderated by Kara Bortone, senior director, Portfolio and Sourcing Management, Johnson & Johnson Innovation – JLABS, focused on topics such as how these startups approached the market and regulatory processes as well as the up-and-coming trends in health technology.

A pediatric anesthesiologist, Dr. Finkel explained the significance of achieving real-time, objective pain measurement. “Pain is one word that represents a myriad of conditions,” she says. “Pain from acute post-operative conditions is very different from peripheral neuropathic pain and different from the type of inflammatory pain seen in lupus and rheumatoid arthritis. Being able to discern the drivers of pain, the etiology, is essential to treating it well and to developing better therapeutics in the future.”

Dr. Finkel points out that AlgometRx measures nociception, which is pain fiber activation, and that is also what medications are addressing. “We’re not discounting a patient’s perception of pain, as we recognize that one’s experience of pain is very complex,” she says. “What we aim to measure is the activity being transmitted by the pain nerve and the type of nerve fiber that is doing the transmitting.”

Aiming to identify pain phenotypes is an important part of current AlgometRx development work, says Dr. Finkel, as it could significantly aid clinical decision-making in treating and monitoring patients’ pain. The company’s current regulatory focus is to seek FDA clearance related to its potential use for patients with peripheral neuropathy, which is pain and numbness resulting from damage to the nerves outside of the brain and spinal cord. The company has also identified fibromyalgia cases as a place where the technology could potentially benefit a large number of patients as it considers regulatory clearance targets.

As the COVID-19 pandemic presented many unique challenges to healthcare startups this year, panel participants were asked to discuss the hurdles they faced and how it impacted device development.

Dr. Finkel notes that the pandemic slowed patient enrollment in AlgometRx clinical studies, but also presented some upside. “At first, that had a negative impact, but it wound up being a good thing,” she says. “It gave us a moment to pause, regroup and examine the data we’d already generated. That break gave us improved information and a new, more powerful approach. It changed our trajectory by altering our regulatory path in terms of the order of things in our pipeline, so we’ve been enormously productive.”

As a Johnson & Johnson Innovation Quickfire Children’s Challenge awardee, Dr. Finkel and AlgometRx will be among the first group of startups taking up residence at the new JLABS @ Washington, DC, located on the Children’s National Research & Innovation Campus, when it opens in 2021 at the historic former Walter Reed Army Medical Center site. Along with a one-year residency at the new JLABS @ Washington DC facility,* AlgometRx will receive mentorship from experts at the Johnson & Johnson Family of Companies and grant funding to help support its continued advancement to commercialization.

*Residency at JLABS @ Washington subject to acceptance and execution of a License Agreement with Children’s National.

communication network concept image

Children’s National joins international AI COVID-19 initiative

communication network concept image

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

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

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

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

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

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

Marius George Linguraru

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

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

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

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

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

telemedicine control room

Telehealth and AI reduce cardiac arrest in the cardiac ICU

telemedicine control room

The telehealth command center located a few steps away from the cardiac ICU at Children’s National Hospital.

The cardiac critical care team at Children’s National Hospital has developed an innovative Tele-Cardiac Critical Care model aiming to keep constant watch over the most fragile children with critical heart disease in the cardiac ICU. The system combines traditional remote monitoring and video surveillance with an artificial intelligence algorithm trained to flag early warning signs that a critically ill infant may suffer a serious event like cardiac arrest while recovering from complex cardiac surgery. This second set of eyes helps bedside teams improve patient safety and quality of care.

These high risk post-operative patients are often neonates or small infants born with the most complex and critical congenital heart diseases that require surgery or interventional cardiac catheterization in their first days or weeks of life. At these early stages after crucial cardiac surgery, these patients can decompensate dangerously fast with few outward physical symptoms.

The AI algorithm (T3) monitors miniscule changes in oxygen delivery and identifies any mismatch with a child’s oxygen needs. It also tracks and displays small changes in vital sign trends that could lead to a serious complication. The cardiac ICU command center staff then analyzes additional patient data and alerts the bedside team whenever needed.

The Tele-Cardiac Critical Care program started two years ago. In that time, the program has contributed to a significant decrease in post-operative cardiac arrest for this patient population.

“It’s easy to see how a model  like this could be adapted to other critical care scenarios, including our other intensive care units and even to adult units,” says Ricardo Munoz, M.D., chief of Cardiac Critical Care and executive director of Telehealth. It allows the physicians and nurses to keep constant watch over these fragile patients without requiring a physician to monitor every heartbeat in person for every patient at every hour of the day to maintain optimal outcomes for all of them.”

Dr. Munoz and Alejandro Lopez-Magallon, M.D., medical director of Telehealth and cardiac critical care specialist, presented data from the pilot program at the American Telemedicine Association’s virtual Annual Meeting on June 26, 2020.

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.