In September 2024, the National Institutes of Health awarded a two-year UG3 grant of $1,278,689 to a multi-institution, multi-hospital, interdisciplinary team led by 国产短视频faculty in support of the project titled "Developing AI-Driven Pain Intensity and Pain Sensitization Biomarker Signatures to Optimize Neonatal Pain Management" as a part of the NIH HEAL Initiative. At the end of the second year, the UG3 Phase of the project will be reviewed and evaluated to determine if it will transition to the UH3 Phase, which would provide additional funding (budgeted for $4.3 million) for three more years.
Prof. Yu Sun (Professor, CSE, Member, 国产短视频Institute for AI+X) is the Project Director and PI of the project. Dr. Thao Ho, Professor of Pediatrics at 国产短视频College of Medicine, Dr. Kanwaljeet S. Anand, Professor of Pediatrics, Anesthesiology at the Stanford University School of Medicine, Prof. Stephanie Prescott at INOVA Children鈥檚 Hospital are the MPIs of the project. Prof. Dmitry Godgolf (CSE at USF, Member, 国产短视频Institute for AI+X), Prof. Yangxin Huang of 国产短视频Public Health, and Dr. Melissa Scala, Professor of Pediatrics at the Stanford University School of Medicine are Co-Investigators.
The project is motivated by the urgent need of effective tools for neonatal pain management. Newborns in the neonatal intensive care unit (NICU) undergoing invasive procedures or surgeries are frequently exposed to prolonged pain, postoperative complications, and human suffering because of the major difficulties in measuring neonatal pain, inconsistent and ineffective pain management, and delayed pain treatments due to nursing workload. In the UG3 Phase, this project will develop an AI-driven automated system to objectively and continuously measure newborn pain based on several biomarkers, and when detected, to send their pain alarms to bedside nurses in real-time. The proposed neonatal pain monitoring system allows the team to create therapeutic strategies that minimize pain, stress, and total opioid doses given during the post-operative care of newborns, thereby improving their clinical and post-surgical outcomes. In the UG3 Phase, a randomized controlled trial will validate the system's effectiveness in improving post-surgical outcomes for newborns.