Peer-reviewed article
Early postnatal brain volumes and two-year cognitive outcomes in preterm infants: An exploratory machine learning study.
Fotios Savvopoulos, Sophie de Munck, Gerbrich E van den Bosch, Nienke Bouw, Jeroen Dudink et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study tested whether brain MRI scans taken from preterm infants around 30 weeks after birth could predict their cognitive and motor development at age two. The researchers used machine learning to analyse brain volumes combined with clinical information from 63 infants for cognitive outcomes and 41 for motor outcomes. Brain volume alone showed modest ability to predict cognitive outcomes but adding clinical information did not improve predictions, and the models could not reliably predict motor outcomes.
Key points
- Machine learning with volumetric brain features alone achieved 0.64 validation accuracy for predicting cognitive outcomes in preterm infants
- Adding clinical characteristics including gestational age, sex, birthweight, and maternal education did not significantly improve prediction accuracy
- Models performed no better than chance when predicting motor outcomes
- The study highlights challenges in applying machine learning to neuroimaging for clinical outcome prediction
- Prediction reliability depends on data quantity and quality, methodological standardisation, and model generalisability
Limitations
The study does not clarify which country or healthcare setting the infants were from, limiting understanding of generalisability. The sample sizes are small (63 for cognitive, 41 for motor outcomes), which may affect the reliability of the machine learning models tested.
Abstract
INTRODUCTION: Premature birth is associated with an increased risk of cognitive and motor developmental problems. Early prediction of these outcomes is crucial for timely intervention and adequate follow-up. This study aimed to predict cognitive and motor outcomes in preterm-born infants using brain volumetric features in combination with clinical and demographic perinatal characteristics, and to compare the performance of three different machine learning algorithms. METHODS: 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. We evaluated the performance of logistic regression, linear support vector classifier (linear SVC), and k-nearest neighbour (KNN). RESULTS: Employing volumetric features only, KNN achieved a validation accuracy of 0.64, showing modest predictive power for cognitive outcomes. Adding clinical characteristics did not significantly improve accuracy. The models did not perform better than chance when predicting motor outcomes. CONCLUSION: Volumetric features demonstrated modest predictive ability for cognitive outcomes, and the addition of clinical features did not significantly improve performance. The low to moderate overall validation accuracy underscores, however, the challenges faced in the clinical application of machine learning in neuroimaging. The reliability and clinical applicability of predictions of outcomes from advanced neuroimaging data will largely depend on the quantity and quality of the data, methodological standardisation, and the generalisability of the prediction models.
Findings the Hub has extracted
Each claim below is labelled by its type of design and links back to this report.
- 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
- Population
- Preterm-born infants (born before 30 weeks gestation); N=63 for cognitive outcomes, N=41 for motor outcomes
Key finding
KNN with volumetric features only achieved validation accuracy of 0.64 for cognitive outcomes, showing modest predictive power
Savvopoulos et al. · PubMed (US National Library of Medicine) · 10 Oct 2026