Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice

What is this project about?    

This study aims to innovate a new way to approach infant pain assessment in Neonatal Intensive Care Units (NICUs) around the world by: a) developing a machine learning algorithm that can discriminate pain-related distress, distress unrelated to pain, and no distress; and b) examining health professionals' and parents’ perspectives on computer-assisted pain assessment as well as the ethical-legal implications surrounding implementation of this technology in healthcare.  

How will we go about doing this project?    

Our goal is to recruit 300 preterm infants born at Mount Sinai Hospital (Canada) and 100 preterm infants born at University College London Hospital (UK). Each infant will participate in an approximately 3-hour observation period, during which the infant will receive a routine heel lance. We will be focusing on heart rate, oxygen saturation, brain electrical activity [EEG], and facial activity to discriminate between different types of distress.  

We are also exploring the socio-cultural and ethical-legal implications of computer-assisted pain assessment by conducting interviews with NICU health professionals and parents.   

What will be done with the research findings?    

The results of this study will provide us with increased knowledge surrounding the need for better pain assessment, a new way to assess infant pain, a better understanding of the stakeholder and ethical-legal context involved with computer-assisted infant pain assessment and also the potential to integrate our algorithm in existing bedside monitors.  

What is the next step?   

We are currently recruiting infants born between 28 to 32 weeks gestational age, who are within 2 weeks postnatal age, and their caregiver from Mount Sinai Hospital and University College London Hospital.  

Want to know more about this project?    

For more information, please feel free to contact Dr. Rebecca Pillai Riddell (, Principal Investigator on the study.