Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis

Background Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS) and often leads to poor outcomes. Currently, there is no ideal tool for early prediction of HT risk. Recently, machine learning has gained traction in stroke management, prompting the exploration of predictive models for HT. However, systematic evidence on these models is lacking. Objective In this study, we assessed the predictive capability of machine learning models for HT risk in AIS, aiming to inform the development of HT prediction tools. Methods We conducted a thorough search of medical databases, such as Web of Science, Embase, Cochrane, and PubMed up until March 2025. The risk of bias was determined through the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analysis was performed based on treatment backgrounds, diagnostic criteria, and types of HT. Results A total of 83 eligible articles were included, containing 106 models and 88,197 patients with AIS with 9323 HT cases. There were 104 validation sets with a total c-index of 0.832 (95% CI 0.814-0.849), sensitivity of 0.82 (95% CI 0.79-0.84), and specificity of 0.78 (95% CI 0.74-0.81). Subgroup analysis indicated that the combined model achieved superior prediction accuracy. Moreover, we also analyzed the predictive performance of 6 mature models. Conclusions Currently, although several prediction methods for HT have been developed, their predictive values are not satisfactory. Fortunately, our findings suggest that machine learning methods, particularly those combining clinical features and radiomics, hold promise for improving predictive accuracy. Our meta-analysis may provide evidence-based guidance for the subsequent development of more efficient clinical predictive models for HT. Trial Registration PROSPERO CRD42024498997; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024498997