Neuroanesthesia & neurocritical care
Automated detection of severe cerebral edema using explainable deep transfer learning after hypoxic ischemic brain injury
Authors' conclusion
Our results support the feasibility of automated SCE detection.
Study context
- Population
- Cardiac arrest patients comatose after restoration of spontaneous circulation (hypoxic ischemic brain injury)
- Participants
- 288 participants
- Comparison
- Automated machine learning (ML) detection of severe cerebral edema (SCE) vs ground-truth SCE from radiology reports
- Outcome
- Detection of severe cerebral edema (SCE)
Methodological detailsmachine learning, cross-validated
- Machine learning
- Cross-validated
Performance
As reported in the abstract
Cross-validated testing accuracy for detecting SCE
Cross-validated testing accuracy of the model for detecting SCE
87%
95% CI 84%, 89%
Further findings
Attention maps explaining SCE classification
- Focused on cisternal regions
- P < 0.05
Predictors of poor neurologic outcome (multivariable)
- Older age
- P < 0.001
- Non-shockable initial cardiac rhythm
- P = 0.004
- Greater ML_SCE values
- P < 0.001
- GT_SCE (non-significant covariate)
- P = 0.064
Original publication
Automated detection of severe cerebral edema using explainable deep transfer learning after hypoxic ischemic brain injury. Resuscitation. Published 23 May 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →