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The problem
Intervention: Machine learning analysis of brain volumetric features from MRI scans acquired at approximately 30 weeks postmenstrual age, combined with clinical and demographic perinatal characteristics, to predict cognitive and motor outcomes at two years corrected age
Who it was tested with: Preterm-born infants (born before 30 weeks gestation); N=63 for cognitive outcomes, N=41 for motor outcomes.
Experience from people who deliver programmes: stories, case studies and lessons learned. Valuable for understanding how and why, and for spotting what to test next, but not a measure of effect.
"We acquired quantitative structural brain MRI at approximately 30 weeks postmenstrual age (PMA) and clinical and demographic birth characteristics including gestational age, sex, birthweight, and maternal education level. Machine learning techniques were applied to predict cognitive (N = 63) and motor (N = 41) outcomes at two years corrected age, assessed using the Bayley Scales of Infant and Toddler Development III, in infants born before 30 weeks gestation." The study applies machine learning algorithms prospectively to neuroimaging and clinical data to predict developmental outcomes, measuring both volumetric features alone and in combination with clinical variables.
Small sample sizes (N=63 cognitive, N=41 motor), exploratory nature of the study, unclear generalisability across different populations and clinical settings, no independent external validation cohort reported
Across countries and designs
1 study · 0 countries
