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.
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.
Abstract:
Up to 40% of very preterm infants (≤32 weeks' gestational age) were identified with a cognitive deficit at 2 years of age. Yet, accurate clinical diagnosis of cognitive deficit cannot be made until early childhood around 3-5 years of age. Recently, brain structural connectome that was constructed by advanced diffusion tensor imaging (DTI) technique has been playing an important role in understanding human cognitive functions. However, available annotated neuroimaging datasets with clinical and outcome information are usually limited and expensive to enlarge in the very preterm infants' studies. These challenges hinder the development of neonatal prognostic tools for early prediction of cognitive deficit in very preterm infants. In this study, we considered the brain structural connectome as a 2D image and applied established deep convolutional neural networks to learn the spatial and topological information of the brain connectome. Furthermore, the transfer learning technique was utilized to mitigate the issue of insufficient training data. As such, we developed a transfer learning enhanced convolutional neural network (TL-CNN) model for early prediction of cognitive assessment at 2 years of age in very preterm infants using brain structural connectome. A total of 110 very preterm infants were enrolled in this work. Brain structural connectome was constructed using DTI images scanned at term-equivalent age. Bayley III cognitive assessments were conducted at 2 years of corrected age. We applied the proposed model to both cognitive deficit classification and continuous cognitive score prediction tasks. The results demonstrated that TL-CNN achieved improved performance compared to multiple peer models. Finally, we identified the brain regions most discriminative to the cognitive deficit. The results suggest that deep learning models may facilitate early prediction of later neurodevelopmental outcomes in very preterm infants at term-equivalent age.
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