Harnessing AI for Children’s Health

Physician scientists and researchers from Children’s National Hospital and Virginia Tech are intensifying their efforts to leverage artificial intelligence (AI) to assist children facing medical challenges.

The innovators will convene in September at the Children’s National Research & Innovation Campus in Washington, D.C.

“It’s clear that harnessing the power of artificial intelligence is the way forward in advancing children’s health,” stated Lance Collins, vice president and executive director of the Virginia Tech Innovation Campus in Alexandria. “Virginia Tech researchers are building momentum and solidifying our collaborative goals in this important area.”

Collaboration and Goals

This initiative involves Virginia Tech’s Sanghani Center for Artificial Intelligence and Data Analytics, Children’s National Hospital, and the Fralin Biomedical Research Institute at VTC, which operates labs at the Children’s National Research & Innovation Campus.

This meeting builds on the success of last year’s workshop, which included discussions on smart surgery, rare diseases, and emergency medicine featuring talks from both Virginia Tech and Children’s National faculty and researchers.

“Now we are expanding the scope of this collaboration to more units at Children’s National Hospital and Virginia Tech,” remarked co-organizer Naren Ramakrishnan, director of the Sanghani Center and the Thomas L. Phillips Professor in the College of Engineering. “We will hear from new groups from Children’s National Hospital, and we will have more Virginia Tech participants joining from areas such as security, conversational AI, and federated learning.”

Removing Barriers

The organizers aim to eliminate barriers between clinicians and AI scientists.

“The rapid evolution of AI technology is unlocking unprecedented possibilities to transform pediatric health care,” said co-organizer Marius George Linguraru, a global leader in utilizing imaging and machine learning to enhance children’s health and the Connor Family Professor of Research and Innovation at Children’s National. “AI’s potential to provide life-changing solutions for children with rare medical conditions is immense, and it’s essential that we collaborate with clinicians, AI scientists, and partner organizations to tap into this potential. Together, we must develop AI tools specifically designed for the unique needs of children—beyond merely adapting models built for adults—to shape the future of pediatric medicine.”

Exploring New Technologies

Collaboration between clinicians and AI scientists was initiated after Michael Friedlander, vice president for health sciences and technology at Virginia Tech, introduced the leadership of the Sanghani Center to teams at Children’s National Hospital.

This laid the groundwork for further exploration of how this technology can be utilized to assist both children and adults.

“We are taking the next steps to explore how new technology can be integrated into clinical practice to enhance our intelligence and decision-making regarding diagnostics, therapeutics, and implementation,” Friedlander explained. “AI-based tools have significantly improved our ability to comprehend complex health data to benefit patients, and they will increasingly be employed to analyze personal health data to predict and ultimately prevent issues long before they arise.”

Project Updates

This year’s session will also provide updates on the progress of five projects jointly supported by Virginia Tech and Children’s National, including:

• Predicting single-cell responses to genetic perturbations in pediatric developmental disorders: AI models predicting how single cells respond to genetic changes could help overcome challenges in studying pediatric developmental disorders, especially those involving rare cell types. Principal investigators are Wei Li, assistant professor, Center for Genetic Medicine, Children’s National Hospital, and Jia-Ray Yu, assistant professor at Virginia Tech’s Fralin Biomedical Research Institute Cancer Research Center — D.C.
• Forecasting emergency department surges: Emergency department crowding leads to surges in patient numbers, system breakdowns, lower satisfaction, and higher rates of patients who leave without being seen. The proposed solution is to develop forecasting models to predict emergency room surges to use backup resources more effectively. Principal investigators are Kenneth McKinley, assistant professor at Children’s National Hospital, and Patrick Butler, a senior research associate at Virginia Tech’s Sanghani Center.
• Improving accuracy in identifying rare genetic syndromes in children through generative models: Identifying rare genetic syndromes in children is challenging. Researchers propose using facial analysis and diffusion models, a type of technology that excels at creating realistic images with minimal data, to simulate disease traits and better detect and classify genetic syndromes. Co-principal investigators are Yanardag Delul, assistant professor in the Department of Computer Science at Virginia Tech, and Xinyang Liu, staff scientist at Precision Medical Imaging lab of Children’s National Hospital.
• Rethinking privacy in federated learning: Sharing data is crucial for training large-scale deep learning models in health care, but privacy concerns hinder the practice, especially in pediatric health care involving rare diseases, where datasets are limited. This project proposes a federated-learning approach, allowing individual patients to collaboratively train a large deep-learning model without sharing their individual data. Principal investigators are Wenjing Lou, the W. C. English Endowed Professor of Computer Science, Virginia Tech, and Syed Muhammad Anwar at the Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital.
• Weakly-supervised clinical variables extraction for sepsis research with large language models: Pediatric sepsis is a major cause of child mortality worldwide and requires advanced strategies for prediction and prevention. This project aims to develop a method to automatically extract clinical variables from documents, radiology reports, and pediatric emergency provider notes for better prediction of sepsis risks. Principal investigators are Xuan Wang, assistant professor of computer science at Virginia Tech, and Ioannis Koutroulis, research director of emergency medicine at Children’s National Hospital.