Doha, November 11 (QNA) - Sidra Medicine, a member of Qatar Foundation, has contributed to a major international study demonstrating how artificial intelligence (AI) models originally developed for adult patients can be adapted to support neonatal care.
Published in the high-impact journal, Nature Biomedical Engineering, the research highlights a promising approach to improving the accuracy of AI-assisted medical imaging for newborns.
Titled "Repurposing Adult AI Radiograph Models for Neonatal Care Through Continual Learning: An International Multicenter Study," the study featured input from 30 neonatal intensive care units across 20 countries and territories. Conducted through the Medical AI Data for All (MAIDA) initiative at Harvard Medical School, the research examined how existing AI models could be adapted to meet the specific needs of newborn patients while protecting patient data privacy.
Senior Attending Physician, Director of the Pediatric Neurosurgery Fellowship program at Sidra Medicine, and co-author of the study Dr. Khalid Al-Kharaz said: "AI has tremendous potential to support clinical decision-making, but its effectiveness depends on how well it performs across different patient populations. Newborns have unique anatomical and clinical needs that cannot always be addressed by technologies developed for adults. This study demonstrates how existing AI models can be adapted to better support neonatal care, potentially helping clinicians assess medical images more accurately and identify issues that require timely attention."
Manager of Imaging Information Systems at Sidra Medicine and co-author of the study Dr. Khalid Al Kharaz said: "Sidra Medicine's participation in this international research reflects our commitment to advancing medical innovation through collaboration with leading institutions worldwide. By contributing our expertise and clinical imaging data, we are supporting research that addresses an important gap in pediatric healthcare. The findings also highlight how international partnerships can accelerate the development of AI technologies that are more inclusive of children and their specific healthcare needs."
Chair of Radiology at Sidra Medicine Dr. Abdusamea Shabani, who selected the radiographs for the study, said: "The study represents an important step toward addressing the limitations of AI technologies developed primarily for adult populations. Its findings suggest that continual learning could provide a practical approach for adapting existing medical imaging models to pediatric settings while maintaining patient data privacy."
The research focused on assessing the positioning of endotracheal tubes, which are used to support breathing in critically ill newborns. Accurate tube placement is essential in neonatal intensive care, as incorrect positioning can lead to serious complications. Researchers investigated whether AI models originally trained to assess tube placement in adults could be adapted to identify anatomical landmarks and detect potentially incorrect tube positioning in newborns. 
Using a technique known as continual learning, the research team adapted an existing AI model through sequential training using neonatal chest X-rays from participating hospitals. Each institution contributed 100-300 radiographs, allowing the model to learn from diverse patient populations without requiring the central pooling of medical imaging data. As one of the participating institutions, Sidra Medicine contributed 100 anonymized neonatal chest X-ray images to support the research. (QNA)