Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.

Journal: Pediatric research
Published Date: Aug 19, 2025

Abstract

Neonatal Encephalopathy (NE) from presumed hypoxic-ischemic encephalopathy (pHIE) is a leading cause of morbidity and mortality in infants worldwide. Recent advancements in HIE research have introduced promising tools for improved screening of high-risk infants, time to diagnosis, and accuracy of assessment of neurologic injury to guide management and predict outcomes, some of which integrate artificial intelligence (AI) and machine learning (ML). This review begins with an overview of AI/ML before examining emerging prognostic approaches for predicting outcomes in pHIE. It explores various modalities including placental and fetal biomarkers, gene expression, electroencephalography, brain magnetic resonance imaging and other advanced neuroimaging techniques, clinical video assessment tools, and transcranial magnetic stimulation paired with electromyography. Each of these approaches may come to play a crucial role in predicting outcomes in pHIE. We also discuss the application of AI/ML to enhance these emerging prognostic tools. While further validation is needed for widespread clinical adoption, these tools and their multimodal integration hold the potential to better leverage neuroplasticity windows of affected infants. IMPACT: This article provides an overview of placental pathology, biomarkers, gene expression, electroencephalography, motor assessments, brain imaging, and transcranial magnetic stimulation tools for long-term neurodevelopmental outcome prediction following neonatal encephalopathy, that lend themselves to augmentation by artificial intelligence/machine learning (AI/ML). Emerging AI/ML tools may create opportunities for enhanced prognostication through multimodal analyses.

Authors

  • Vonita Chawla
    Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA. vchawla@uams.edu.
  • Mehmet N Cizmeci
    Division of Neonatology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
  • Kelsey M Sullivan
    Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Emily C Gritz
    Department of Pediatrics, Connecticut Children's Medical Center, Hartford, CT, USA.
  • Vilmaris Q Cardona
    Department of Pediatrics, St. Christopher's Hospital for Children, Philadelphia, PA, USA.
  • Ogechukwu Menkiti
    Department of Pediatrics, St. Christopher's Hospital for Children, Philadelphia, PA, USA.
  • Girija Natarajan
    Department of Pediatrics, Children's Hospital of Michigan, Detroit, MI, USA.
  • Rakesh Rao
    Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA.
  • Ryan M McAdams
    Division of Neonatology, Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA. mcadams@pediatrics.wisc.edu.
  • Maria Lv Dizon
    Department of Pediatrics, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.

Keywords

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