Prognostic value of machine learning for brain computed tomography as a predictor of neurologic outcomes after cardiac arrest: a systematic review and meta-analysis
Machine learning in brain CT may have significant diagnostic value for predicting poor neurological outcomes in post-cardiac arrest patients. Machine learning may be comparable to automatic GWR measurement and outperform manual GWR measurement in terms of diagnostic accuracy.
Authors
- Juncheol Lee
- Jae‐Joong Kim (ORCID: https://orcid.org/0000-0002-2714-2282)
- B S Kim (ORCID: https://orcid.org/0009-0004-6774-3543)
- W. Kim
- Jaehoon Oh
- Hyun-Young Choi
- Kyung Hun Yoo
- Elleah Jueun Chin
Institutions
- Hallym University (KR)
- Samsung (South Korea) (KR)
- Kangnam Sacred Heart Hospital (KR)
- Hallym University Kangnam Sacred Heart Hospital (KR)
- Hanyang University (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Scandinavian Journal of Trauma Resuscitation and Emergency Medicine
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1186/s13049-026-01565-w
- Primary Topic
- Cardiac Arrest and Resuscitation
- Type
- article
- Field-Weighted Citation Impact
- 0.00