Neuroprognostication via Spatially-Informed Machine Learning Following Hypoxic-Ischemic Injury
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Key Points
Question
Can machine learning be used to reliably and accurately predict 18-month developmental outcomes from neonatal brain MRI following perinatal hypoxic-ischemic injury (HIE)?
Findings
In this cohort study we show that across cognitive, language, and motor domains, a machine learning model can predict 18-month developmental outcome scores for neonates with HIE with excellent accuracy, and can produce atlases of the brain regions responsible for developmental impairments.
Meaning
Machine learning can be used for automated neuroprognostication in HIE, and may not only produce accurate predictions, but also provide neuroanatomical information that may prove useful in the search for novel interventions.
Importance
Perinatal hypoxic-ischemic encephalopathy (HIE) is one of the most common causes of neonatal death and neurodevelopmental impairment worldwide. Accurate prognostication of developmental outcomes following perinatal HIE is an important component of family-centered and evidence-based care.
Objective
To utilize magnetic resonance imaging (MRI)-based radiomic measures together with machine learning to produce automated and objective predictions of developmental outcomes after perinatal HIE.
Design
This was a retrospective cohort study of infants born between January 2018 and January 2022 with HIE.
Setting
The data for this study were acquired at the neonatal neurocritical care unit of a quaternary care center based on the center’s institutional criteria for diagnosis and for the use of therapeutic hypothermia.
Participants
Neonates with a gestational age of ≥ 35 weeks and a diagnosis of neonatal encephalopathy.
Exposure(s)
Therapeutic hypothermia, with a whole-body cooling system, was begun within 6 hours after birth and was continued for 72 hours.
Main Outcome(s) and Measure(s)
Brain MRI data were acquired on postnatal day 4-5, after rewarming after completion of therapeutic hypothermia. At 18-months of age, developmental outcome measures were assessed with the Bayley Scales of Infant and Toddler Development. We extracted radiomic measures from the deep-gray matter structures and from 2224 cubic tiles across the entire brain, in multiple modalities, and provided these measures to an elastic-net penalized linear regression model to predict the 18-month developmental outcomes.
Results
MRI-based radiomic measures from 160 neonates were used in a 10-fold cross-validation framework to predict the 18-month Bayley outcome scores. Across cognitive, language, and motor domains, the mean correlation between the predicted outcomes and the observed outcomes was 0.947, and the mean coefficient of determination was 0.879.
Conclusions and Relevance
A machine learning model using MRI-based radiomic measures from infants with HIE can reliably predict their 18-month developmental outcomes with excellent accuracy across the full range of motor, cognitive, and language domains. In addition, our approach allowed us to map the predictor weightings into neuroanatomical space, producing atlases of the brain regions responsible for the developmental impairments; these may prove useful in the search for novel interventions.
