AI Article Synopsis

  • Neonatal hypoxic-ischemic encephalopathy is a severe brain condition in infants, often leading to death or developmental issues, and MRI is used for prognosis, but with high variability in predictions.
  • Researchers developed an automated method to analyze MRI data from 286 infants, incorporating anatomical templates and advanced measurements to enhance outcome predictions.
  • The study found that MRI-based analysis could predict neurodevelopmental outcomes more accurately than traditional demographic and lab data alone, indicating that machine learning can significantly improve prognosis for affected infants.

Article Abstract

Objectives: Neonatal hypoxic-ischemic encephalopathy is a serious neurologic condition associated with death or neurodevelopmental impairments. Magnetic resonance imaging (MRI) is routinely used for neuroprognostication, but there is substantial subjectivity and uncertainty about neurodevelopmental outcome prediction. We sought to develop an objective and automated approach for the analysis of newborn brain MRI to improve the accuracy of prognostication.

Methods: We created an anatomic MRI template from a sample of 286 infants treated with therapeutic hypothermia, and labeled the deep gray-matter structures. We extracted quantitative information, including shape-related information, and information represented by complex patterns (radiomic measures), from each of these structures in all infants. We then trained an elastic net model to use either only these measures, only the infants' demographic and laboratory data, or both, to predict neurodevelopmental outcomes, as measured by the Bayley Scales of Infant and Toddler Development at 18 months of age.

Results: Among those infants for whom Bayley scores were available for cognitive, language, and motor outcomes, we found sets of MRI-based measures that could predict their Bayley scores with correlations that were greater than the correlations based on only the demographic and laboratory data, explained more of the variance in the observed scores, and generated a smaller error; predictions based on the combination of the demographic-laboratory and MRI-based measures were similar or marginally better.

Interpretation: Our findings show that machine learning models using MRI-based measures can predict neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy across all neurodevelopmental domains and across the full spectrum of outcomes. ANN NEUROL 2024.

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Source
http://dx.doi.org/10.1002/ana.27154DOI Listing

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