Actively Recruiting
Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice
Led by York University · Updated on 2022-10-13
400
Participants Needed
2
Research Sites
317 weeks
Total Duration
On this page
Sponsors
Y
York University
Lead Sponsor
M
MOUNT SINAI HOSPITAL
Collaborating Sponsor
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Infants born between 28 0/7 weeks and 32 6/7 weeks gestational age
- Infants within 6 weeks postnatal age
- Infants undergoing a routine heel lance procedure
- Parents of a child currently in the NICU or health professionals currently working in the NICU (for qualitative interviews)
You will not qualify if you...
- Infants with congenital malformations
- Infants currently receiving analgesics or sedatives at the time of study, except sucrose
- Infants with a history of perinatal hypoxia or ischemia at the time of study
- Infants with diaper rash or excoriated buttocks
- Participants who cannot communicate fluently in English (for qualitative interviews)
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 2 locations
1
Mount Sinai Hospital
Toronto, Ontario, Canada, M5G 1X5
Actively Recruiting
2
University College London Hospital
London, No Province, United Kingdom, N1 2EP
Actively Recruiting
Research Team
R
Rebecca Pillai Riddell, PhD
CONTACT
L
Lorenzo Fabrizi, PhD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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