Journal article
Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence
Pediatric research, v 98(7), pp 2554-2564
01 Dec 2025
PMID: 40830411
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence
- Creators
- Vonita Chawla - University of Arkansas for Medical SciencesMehmet N Cizmeci - SickKids FoundationKelsey M Sullivan - University of Pittsburgh School of MedicineEmily C Gritz - Connecticut Children's Medical CenterVilmaris Q Cardona - St. Christopher's Hospital for ChildrenOgechukwu Menkiti - Drexel University, PediatricsGirija Natarajan - Children's Hospital of MichiganRakesh Rao - Washington University in St. LouisRyan M McAdams - University of Wisconsin–MadisonMaria Lv Dizon - Lurie Children's HospitalHIE focus group of the Children’s Hospitals Neonatal Consortium
- Publication Details
- Pediatric research, v 98(7), pp 2554-2564
- Publisher
- Pediatric research
- Number of pages
- 10
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Pediatrics
- Web of Science ID
- WOS:001552561900001
- Scopus ID
- 2-s2.0-105016593389
- Other Identifier
- 991022084553504721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Pediatrics