Physician scientists and researchers at Children’s National Hospital and Virginia Tech will increase efforts to harness artificial intelligence (AI) to help children with health problems.
Innovators will meet in September at National Children’s Research and Innovation Campus in Washington, D.C.
“It’s clear that harnessing the power of artificial intelligence is the path forward to advancing children’s health,” said Lance Collins, vice president and executive director of Virginia Tech. Innovation Campus in Alexandria. “Virginia Tech researchers are building momentum and solidifying our collaborative goals in this important area. »
This effort involves Virginia Tech Sanghani Center for Artificial Intelligence and Data AnalyticsNational Children’s Hospital and VTC’s Fralin Biomedical Research Institute, which has laboratories on the National Children’s Research and Innovation Campus.
This meeting builds on the momentum of last year’s meeting workshopwhich included sessions on smart surgery, rare diseases and emergency medicine with presentations from Virginia Tech and Children’s National faculty and researchers.
“We are now expanding the scope of this collaboration to more units at Children’s National Hospital and Virginia Tech,” said co-organizer Naren Ramakrishnan, director of the Sanghani Center and the Thomas L. Phillips Professor in the College of Engineering. “We’ll hear from new groups from Children’s National Hospital, and we’ll welcome more people from Virginia Tech in areas like security, conversational AI and federated learning.”
Organizers seek to break down barriers between clinicians and AI scientists.
“The rapid evolution of AI technology is opening up unprecedented possibilities to transform pediatric healthcare,” said co-organizer Marius George Linguraru, a global leader in harnessing the power of imaging and machine learning to advance children’s health and Connor Family Research and Research Professor. Innovation at Children’s National. “The potential for AI to deliver life-changing solutions for children with rare diseases is immense, and it is essential that we collaborate with clinicians, AI scientists and partner organizations to harness this potential. Together, we must create AI tools specifically designed for the unique needs of children, beyond simply adapting models designed for adults, to shape the future of pediatric medicine. »
The collaboration between clinicians and AI scientists came to fruition after Michael Friedlander, vice president of health sciences and technology at Virginia Tech, introduced Sanghani Center leadership to teams at Nationwide Children’s Hospital.
This has paved the way for further exploration of how this technology can be used to help children and adults.
“We are taking the next steps to explore how new technologies can be integrated into clinical practice to increase our intelligence and decision-making in diagnosis, therapy and implementation,” Friedlander said. “AI-based tools have significantly improved our ability to understand complex health data for the benefit of patients, and they will increasingly be used to understand a person’s personal health data to predict and eventually prevent a problem well before it occurs.
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 disruptions in pediatric developmental disorders: AI models predicting how single cells respond to genetic changes could help overcome challenges in studying pediatric developmental disorders, particularly those involving rare cell types. These models would help identify potential treatments, thereby filling the gaps in current approaches to pediatric genetic diseases. The principal investigators are Wei Li, assistant professor, Center for Genetic Medicine, Nationwide Children’s Hospital, and Jia-Ray Yu, assistant professor at the Fralin Biomedical Research Institute Cancer Research Center — DC at Virginia Tech.
- Predict surges in emergency departments: Overcrowding in emergency departments leads to increased patient volumes, system outages, lower satisfaction, and higher rates of patients leaving without being seen. The proposed solution is to develop forecasting models to predict surges in emergency rooms to more efficiently utilize emergency resources. The principal investigators are Kenneth McKinley, assistant professor at Children’s National Hospital, and Patrick Butler, senior research associate at the Sanghani Center at Virginia Tech.
- Improving the accuracy of identifying rare genetic syndromes in children using generative models: Identifying rare genetic syndromes in children is challenging. The researchers propose using facial analysis and diffusion models, a type of technology for creating realistic images with little 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, a scientist in the Precision Medical Imaging Laboratory at National Children’s Hospital.
- Rethinking Privacy in Federated Learning: Data sharing is crucial for training large-scale deep learning models in healthcare, but privacy concerns hamper the practice, especially in healthcare. pediatric healthcare involving rare diseases, where data sets are limited. This project proposes a federated learning approach, in which individual patients can collaboratively train a large deep learning model without sharing their individual data. The principal investigators are Wenjing Lou, WC English Endowed Professor of Computer Science, Virginia Tech, and Syed Muhammad Anwar of the Sheikh Zayed Institute for Pediatric Surgical Innovation, National Children’s Hospital.
- Mining Weakly Supervised Clinical Variables for Sepsis Research with Large Language Models: Pediatric sepsis is a major cause of childhood mortality worldwide and requires advanced strategies to predict and prevent. This project aims to develop a method to automatically extract clinical variables from documents, radiology reports, and pediatric emergency provider notes for better sepsis risk prediction. The principal investigators are Xuan Wang, assistant professor of computer science at Virginia Tech, and Ioannis Koutroulis, research director of emergency medicine at Children’s Nationwide.