Machine Learning Models Demonstrate Promising Accuracy in Predicting Vasospasm After Aneurysmal Subarachnoid Hemorrhage
A systematic review and meta-analysis published in Acta Neurochirurgica investigated the use of machine learning (ML) to predict vasospasm following aneurysmal subarachnoid hemorrhage (aSAH). Twelve studies (2011-2025) encompassing 25 ML models were included. Deep learning achieved the highest sensitivity (mean 97.6%) and AUC-ROC (0.97), outperforming regression, ensemble, and SVM methods in sensitivity (P = 0.003) but not in specificity or AUC. Across cohort types, deep learning consistently delivered high accuracy and generalizability, although with greater positive predictive value variability. These tools may enable earlier, personalized interventions; however, due to risk of bias, heterogeneity, and limited external validation, prospective trials are needed to support clinical adoption.