Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis
Abstract
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…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health. Reported size: 88,197 participants or records.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.