Automated Neuroprognostication Via Machine Learning in Neonates with Hypoxic‐Ischemic Encephalopathy
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 2025;97:791-802.
Authors
- John D. Lewis (ORCID: https://orcid.org/0000-0002-0832-7396)
- K Raghu (ORCID: https://orcid.org/0000-0002-4042-9907)
- Helen M. Branson (ORCID: https://orcid.org/0000-0002-7429-9012)
- Michelle Stoopler (ORCID: https://orcid.org/0000-0002-9296-5103)
- Ashley Danguecan
- Linh Gia Ly (ORCID: https://orcid.org/0000-0001-7709-4752)
- Brian Thomas Kalish (ORCID: https://orcid.org/0000-0002-3184-018X)
- Mehmet Nevzat Çizmeci (ORCID: https://orcid.org/0000-0001-6807-4266)
- Atiyeh A. Miran (ORCID: https://orcid.org/0009-0005-3435-8283)
Institutions
- University of Toronto (CA)
- Hospital for Sick Children (CA)
- Mental Health Research Canada (CA)
- Zero to Three (US)
- SickKids Foundation (CA)
Publication Details
- Journal
- Annals of Neurology
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1002/ana.27154
- Citations
- 15
- Primary Topic
- Neonatal and fetal brain pathology
- Type
- article
- Field-Weighted Citation Impact
- 16.64
Funders
- TD Bank