@inproceedings{c07c933c820741b4a78e32575860b686,
title = "Investigating brain age deviation in preterm infants: A deep learning approach",
abstract = "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{\textquoteright}s r = 0.6, p < 0.05) from MRIs before 36 weeks of age ({\textquoteleft}Early{\textquoteright} 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 {\textquoteleft}Early{\textquoteright} 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.",
keywords = "Cerebral Palsy, CNN, Deep learning, Postmenstrual age, Preterm",
author = "Susmita Saha and Alex Pagnozzi and Joanne George and Colditz, {Paul B.} and Roslyn Boyd and Stephen Rose and Jurgen Fripp and Kerstin Pannek",
note = "Publisher Copyright: {\textcopyright} Crown 2018.; 1st International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and 3rd International Workshop on Preterm, Perinatal, and Paediatric Image Analysis, PIPPI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 16-09-2018",
year = "2018",
doi = "10.1007/978-3-030-00807-9_9",
language = "English",
isbn = "9783030008062",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "87--96",
editor = "Andrew Melbourne and Roxane Licandro and Matthew DiFranco and Paolo Rota and Melanie Gau and Martin Kampel and Rosalind Aughwane and Pim Moeskops and Ernst Schwartz and Emma Robinson and Roxane Licandro and Antonios Makropoulos",
booktitle = "Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis",
}