Language function following preterm birth: prediction using machine learning

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 term-equivalent 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. Impact A combination of clinical perinatal factors and neonatal DTI measures of white matter microstructure leads to accurate prediction of language outcome at 2 years corrected gestational age following preterm birth. A model that comprises clinical and MRI features that has potential to be scalable across centres. It offers a basis for enhancing the power and generalizability of diagnostic and prognostic studies of neurodevelopmental disorders associated with language impairment. Early identification of infants who are at risk of language delay, facilitating targeted early interventions and support services, which could improve the quality of life for children born preterm.

Corpus callosum abnormalities at term-equivalent age are associated with language development at two years corrected age in infants born very preterm

“…Our findings bear significance in that they demonstrate a robust association between DTI measures and language outcomes, independent of both clinical and social risk factors. This association stands in contrast to previous DTI studies which only controlled for clinical factors 26, 39, 40 . Social factors are widely acknowledged as potent predictors of language outcomes 10, 11 , and this was again validated in our study.…”

“…Additionally, accurately quantifying language development at 2 years is inherently challenging, as evidenced by the limited association between the cognitive and language outcomes at 2 years and later childhood scores in prior studies 50,51 . In contrast, Valavani et al (2021) developed a machine learning model combining clinical factors and neonatal diffusion MRI measures that achieved a higher balanced accuracy of 91% for predicting language deficits at 2 years corrected age in preterm infants 42 . He et al 52 used deep learning and a multimodal MRI approach that included DTI and resting state functional MRI to achieve 88% balanced accuracy in predicting language deficits at age 2.…”

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

“…It has recently become popular to directly train deep-learning-based classifier algorithms to turn raw signals into high-level categorical outputs, such as diagnostic 54 , 55 or clinical outcomes 41 , 56 , 57 . A direct clinical diagnostic prediction from the raw data could have been used in our context as well.…”

Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology

“…Our findings indicate a strong linear relationship between DWMA signal and microscopic white matter alterations that are consistent with diffuse white matter gliosis ( Galinsky et al, 2020 ; Griffith et al, 2012 ; Volpe, 2009 ) and, considering the published evidence presented above, underscore its significance as a biomarker of later neurodevelopmental impairment. Studies in infants born very preterm have reported that several regional microstructural metrics are associated with neurodevelopmental deficits at 2 to 3 years corrected age ( Ball et al, 2017 ; Parikh, 2016 ; Valavani et al, 2021 ; Vassar et al, 2020 ). We recently examined the combination of DWMA with structural and functional connectivity and clinical factors and demonstrated that such a multimodal approach was more accurate than individual modalities at predicting cognitive, language, and motor deficits at age two ( He et al, 2021 ).…”