Language function following preterm birth: prediction using machine learning

Evi Valavani*, Manuel Blesa, Paola Galdi, Gemma Sullivan, Bethan Dean, Hilary Cruickshank, Magdalena Sitko-Rudnicka, Mark E Bastin, Richard Chin, Donald MacIntyre, Sue Fletcher-Watson, James P Boardman, Thanasis Tsanas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Preterm birth can lead to impaired language development. This study aimed to predict language outcomes at
2 years corrected gestational age (CGA) for children born preterm.
METHODS: We analysed data from 89 preterm neonates (median GA 29 weeks) who underwent diffusion MRI (dMRI) at termequivalent age and language assessment at 2 years CGA using the Bayley-III. Feature selection and a random forests classifier were
used to differentiate typical versus delayed (Bayley-III language composite score <85) language development.
RESULTS: The model achieved balanced accuracy: 91%, sensitivity: 86%, and specificity: 96%. The probability of language delay at 2
years CGA is increased with: increasing values of peak width of skeletonized fractional anisotropy (PSFA), radial diffusivity (PSRD),
and axial diffusivity (PSAD) derived from dMRI; among twins; and after an incomplete course of, or no exposure to, antenatal
corticosteroids. Female sex and breastfeeding during the neonatal period reduced the risk of language delay.
CONCLUSIONS: The combination of perinatal clinical information and MRI features leads to accurate prediction of preterm infants
who are likely to develop language deficits in early childhood. This model could potentially enable stratification of preterm children
at risk of language dysfunction who may benefit from targeted early interventions.
Original languageEnglish
JournalPediatric Research
DOIs
Publication statusPublished - 11 Oct 2021

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