Investigating brain age deviation in preterm infants: A deep learning approach

Susmita Saha, Alex Pagnozzi, Joanne George, Paul B. Colditz, Roslyn Boyd, Stephen Rose, Jurgen Fripp, Kerstin Pannek

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

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’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.

Original languageEnglish
Title of host publicationData Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis
Subtitle of host publicationFirst International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018 Held in Conjunction with MICCAI 2018, Proceedings
EditorsAndrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, Roxane Licandro, Antonios Makropoulos
PublisherSpringer
Pages87-96
Number of pages10
ISBN (Electronic)9783030008079
ISBN (Print)9783030008062
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event1st 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 - Granada, Spain
Duration: 16 Sept 201816 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11076 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st 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
Country/TerritorySpain
CityGranada
Period16/09/1816/09/18

Keywords

  • Cerebral Palsy
  • CNN
  • Deep learning
  • Postmenstrual age
  • Preterm

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