Neuroprognostication via Radiomics and Machine Learning Following Neonatal Hypoxic-Ischemic Insult
A machine learning model using only MRI-based radiomic measures from infants with HIE can reliably predict their 18-month developmental outcomes with high accuracy across motor, cognitive, and language domains, regardless of the severity of their brain injury. In addition, our approach allowed us to produce atlases of the brain regions responsible for the developmental impairments, which may prove useful in the search for novel interventions.
Authors
- John D. Lewis (ORCID: https://orcid.org/0000-0002-0832-7396)
- Michelle Stoopler (ORCID: https://orcid.org/0000-0002-9296-5103)
- Ashley Danguecan
- Mehmet Nevzat Çizmeci (ORCID: https://orcid.org/0000-0001-6807-4266)
- Linh G. Ly (ORCID: https://orcid.org/0000-0001-7709-4752)
- Atiyeh A. Miran (ORCID: https://orcid.org/0009-0005-3435-8283)
- Krishna Raghu
- Brian T. Kalish
Institutions
- University of Toronto (CA)
- Hospital for Sick Children (CA)
- Mental Health Research Canada (CA)
- SickKids Foundation (CA)
Publication Details
- Journal
- The Journal of Pediatrics
- Published
- 2026-02-14
- DOI
- https://doi.org/10.1016/j.jpeds.2026.115033
- Primary Topic
- Neonatal and fetal brain pathology
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- TD Bank
- Alliance de recherche numérique du Canada