Rebooting Infant Pain Care: Using Machine Learning and Skin-to-Skin Contact to Exponentially Improve Neonatal Intensive Care Unit Practice

To address the current limitations related to infant pain assessment in the NICU, our international team of knowledge users and health/natural science/engineering/social science researchers have come together to build a machine learning algorithm that will learn how to discriminate invasive and non-invasive distress. Furthermore, to improve the use of current pain management practices, our team seeks to better understand the developmental mechanisms underlying skin-to-skin contact over time and factors that may influence its efficacy in mitigating pain responses in preterm infants. This is an ongoing naturalistic observational study.

NCT Number	NCT05579496
Lead Sponsor	York University Collaborators: MOUNT SINAI HOSPITAL, University College, London, University College London Hospitals
Conditions	Acute Pain
Enrollment	400 participants
Start Date	2020-11-01
Primary Completion	2030-03 (estimated)
Study Completion	2031-03 (estimated)
Updated on ClinicalTrials.gov	2026-07-13