Machine Learning Models for Brain Arteriovenous Malformations Presenting with Hemorrhage Based on Clinical and Angioarchitectural Characteristics

This study aims to develop the best diagnostic model for brain arteriovenous malformations (bAVMs) rupture by using machine learning (ML) algorithms. We retrospectively included 353 adult patients with ruptured and unruptured bAVMs. The clinical and radiological data on patients were collected. The significant variables were selected using univariable logistic regression. We constructed and compared the predictive models using five supervised ML algorithms, multivariable logistic regression, and R2eDAVM scoring system. A complementary systematic review and meta-analysis of studies was aggregated to explore the potential predictors for bAVMs rupture. We found that a small nidus size of