SynthesisScandinavian journal of trauma, resuscitation and emergency medicine2026
Prognostic value of machine learning for brain computed tomography as a predictor of neurologic outcomes after cardiac arrest: a systematic review and meta-analysis.
Synthesis in Scandinavian journal of trauma, resuscitation and emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe gray-to-white matter ratio (GWR) on brain computed tomography (CT) is used to predict neurological outcomes after cardiac arrest. Even though automated methods, such as automatic GWR and machine learning, have been compared to manual GWR, the superiority remains unknown. Therefore, we conducted a systematic review and meta-analysis to compare the diagnostic accuracy of these three CT-based methods.
methodsWe systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and IEEE Xplore and included studies evaluating neurological outcomes using the Cerebral Performance Category Scale. We performed a subgroup analysis to compare machine learning with manual or automatic GWR measurements. The Prediction model Risk of Bias ASsessment Tool was used to assess the risk of bias and applicability. Diagnostic accuracy for predicting poor neurological outcomes was evaluated using the pooled diagnostic odds ratio (DOR) and pooled area under the curve (AUC).
resultsIn total, 1594 patients from seven observational studies were included. Machine learning showed significantly higher diagnostic accuracy (pooled AUC, 0.813; pooled DOR, 14.02; 95% confidence interval [CI], 6.51-30.18; I
conclusionMachine 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.
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