Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer

Ming Chen1,2, Hailong Li1, Jinghua Wang3

  • 1The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.

Frontiers in Neuroscience
|October 12, 2020
PubMed

Insights

A new deep learning model predicts cognitive deficits in very preterm infants using brain scans. This tool aids early diagnosis, improving neurodevelopmental outcomes for high-risk newborns.

Area of Science:

  • Neuroscience and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Neonatal Development and Outcomes

Background:

  • Cognitive deficits affect up to 40% of very preterm infants (≤32 weeks' gestational age), but diagnosis is delayed until early childhood.
  • Brain structural connectomes, derived from diffusion tensor imaging (DTI), offer insights into cognitive functions.
  • Limited annotated neuroimaging datasets in very preterm infants hinder the development of early prognostic tools.

Purpose of the Study:

  • To develop a deep learning model for the early prediction of cognitive deficit in very preterm infants.
  • To utilize brain structural connectomes and transfer learning to overcome data limitations.
  • To identify brain regions critical for predicting cognitive outcomes.

Main Methods:

  • A transfer learning-enhanced convolutional neural network (TL-CNN) model was developed.
  • Brain structural connectomes were constructed from DTI images of 110 very preterm infants at term-equivalent age.
  • The model was applied to classify cognitive deficit and predict continuous cognitive scores using Bayley III assessments at 2 years corrected age.

Main Results:

  • The TL-CNN model demonstrated superior performance compared to peer models for both classification and prediction tasks.
  • The study successfully identified specific brain regions that are most discriminative for cognitive deficit.
  • Deep learning approaches show promise for predicting neurodevelopmental outcomes in very preterm infants.

Conclusions:

  • The developed TL-CNN model enables early prediction of cognitive deficits in very preterm infants using brain connectome data.
  • This approach can aid in timely intervention and management of neurodevelopmental issues in this vulnerable population.
  • The findings highlight the potential of advanced neuroimaging and AI in neonatal prognostication.