Show simple item record

dc.contributor.authorSaha, S.
dc.contributor.authorPagnozzi, A.
dc.contributor.authorGeorge, J.
dc.contributor.authorColditz, P.
dc.contributor.authorBoyd, Roslyn
dc.contributor.authorRose, S.
dc.contributor.authorFripp, J.
dc.contributor.authorPannek, K.
dc.date.accessioned2018-12-13T09:15:04Z
dc.date.available2018-12-13T09:15:04Z
dc.date.created2018-12-12T02:47:11Z
dc.date.issued2018
dc.identifier.citationSaha, S. and Pagnozzi, A. and George, J. and Colditz, P. and Boyd, R. and Rose, S. and Fripp, J. et al. 2018. Investigating brain age deviation in preterm infants: A deep learning approach, pp. 87-96.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/72987
dc.identifier.doi10.1007/978-3-030-00807-9_9
dc.description.abstract

© Crown 2018. 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.

dc.titleInvestigating brain age deviation in preterm infants: A deep learning approach
dc.typeConference Paper
dcterms.source.volume11076 LNCS
dcterms.source.startPage87
dcterms.source.endPage96
dcterms.source.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dcterms.source.seriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dcterms.source.isbn9783030008062
curtin.departmentSchool of Occ Therapy, Social Work and Speech Path
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record