Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in
Hailong Li1, Junqi Wang2, Zhiyuan Li3
1Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Early prediction of neurodevelopmental deficits in very preterm infants is crucial. A novel graph convolutional network (GCN) model with supervised contrastive learning (SCL) accurately predicts these deficits using brain structural connectomes, enabling timely interventions.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Pediatrics
Background:
- Very preterm (VPT) infants face high risks of neurodevelopmental deficits, often diagnosed late.
- Early prediction is vital for timely interventions, but current methods are limited.
- Brain structural connectome (SC) analysis shows promise but requires advanced modeling.
Purpose of the Study:
- To develop and evaluate deep learning models for early prediction of neurodevelopmental deficits in VPT infants.
- To leverage graph convolutional networks (GCNs) for analyzing graph-structured SC data.
- To enhance GCN model performance using supervised contrastive learning (SCL) for data scarcity.
Main Methods:
- Applied GCN models to brain SC data from VPT infants at term-equivalent age.
- Utilized SCL to improve model robustness and handle limited data.
- Trained and validated models on a cohort of approximately 280 VPT infants from the CINEPS study.
- Assessed neurodevelopmental outcomes (cognition, language, motor skills) at 2 years corrected age.
Main Results:
- The SCL-enhanced GCN model achieved AUCs of 0.72–0.75 for predicting neurodevelopmental deficits.
- This performance surpassed several competing models, demonstrating the effectiveness of the approach.
- The results validate the hypothesis that SCL improves GCN-based prediction accuracy.
Conclusions:
- SCL significantly enhances GCN models for predicting neurodevelopmental deficits in VPT infants.
- This approach offers a promising tool for early identification and intervention.
- Accurate, early prediction of neurodevelopmental outcomes is achievable using advanced AI on brain SC data.
More Related Videos
11:14A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
Published on: October 4, 2015
08:51Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
