The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis

Objectives: This study aimed to assess the accuracy of artificial intelligence (AI) models in predicting the prognosis of stroke. Methods: PubMed, Embase, and Web of Science were searched from inception to February 2023 for cohort studies using AI to predict acute stroke prognosis. Study quality was assessed with QUADAS, and a random/fixed-effects approach was used to summarize data. The area under the receiver operating characteristic curve (AUC) was used as the accuracy metric. Results: Of 1,241 publications, seven studies were included (17 AI models). Risk of bias was low and heterogeneity was not significant. The pooled AUC under a fixed-effects model was 0.872 (95% CI 0.862–0.881). Subgroup AUCs: deep learning (DL) 0.888 (95% CI 0.872–0.904), logistic regression (LR) 0.852 (95% CI 0.835–0.869), random forest (RF) 0.863 (95% CI 0.845–0.882), support vector machine (SVM) 0.905 (95% CI 0.857–0.952), and XGBoost 0.905 (95% CI 0.805–1.000). Conclusion: AI models demonstrate good accuracy for predicting outcomes in ischemic stroke and can assist clinicians. Continued algorithmic advances and larger datasets may further improve performance.

Yujia Yang, Li Tang, Yiting Deng, Xuzi Li, Anling Luo, Zhao Zhang, Li He, Cairong Zhu, Muke Zhou, Ni Zhang, Xia Zhang, Qifu Li