Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach
This study examined postmenstrual age (PMA) estimation (in weeks) from brain diffusion MRI of very preterm born infants (born <31weeks gestational age), with an objective to investigate how differences in estimated brain age and PMA were associated with the risk of Cerebral Palsy disorders (CP). Infants were scanned up to 2 times, between 29 and 46 weeks (w) PMA. We applied a deep learning 2D convolutional neural network (CNN) regression model to estimate PMA from local image patches extracted from the diffusion MRI dataset. These were combined to form a global prediction for each MRI scan. We found that CNN can reliably estimate PMA (Pearson’s r = 0.6, p < 0.05) from MRIs before 36 weeks of age (‘Early’ scans). These results revealed that the local fractional anisotropy (FA) measures of these very early scans preserved age specific information. Most interestingly, infants who were later diagnosed with CP were more likely to have an estimated younger brain age from ‘Early’ scans, the estimated age deviations were significantly different (Regression coefficient: −2.16, p < 0.05, corrected for actual age) compared to those infants who were not diagnosed with CP.
KeywordsPreterm CNN Cerebral Palsy Postmenstrual age Deep learning
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