A multi-national multidisciplinary team will be working collaboratively to build a machine learning algorithm to distinguish between preterm infant distress states in the Neonatal Intensive Care Unit.
QUALITATIVE INTERVIEWS
parents of a child currently in the NICU or
health professionals currently working in the NICU.
Participants who cannot communicate fluently in English
QUANTITITATIVE DATA CAPTURE (video, eeg, ecg, SPo2)
Infants born between 28 0/7 weeks 32 6/7 weeks gestational age
Infants who are within 6 weeks postnatal age
Preterm infants
Cohort
Prospective
Not listed
Behavioural Correlate of Distress
To be analyzed using machine learning via bedside videography: Facial Grimacing using Neonatal Facial Coding System(NFCS-P subset; Bucsea et al., in preparation)
Cortical Correlate of Distress
To be analyzed using machine learning via bedside monitoring: Continuous EEG data capture
Cardiac Correlates of Distress
To be analyzed using machine learning via bedside monitoring: Heart Rate, Heart Rate Variability
Oxygen Saturation Correlate of Distress
To be analyzed using machine learning via bedside monitoring: amount of oxygen-carrying hemoglobin in the blood relative to the amount of hemoglobin not carrying oxygen
Semi-Structured Interview
Health Professionals and Caregivers will be asked about their thoughts on using AI for infant pain assessment
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York University
Lead sponsor
MOUNT SINAI HOSPITAL
Collaborator
University College, London
Collaborator
University College London Hospitals
Collaborator
University of Calgary
Collaborator
McMaster University
Collaborator
