The role of artificial intelligence in estimating stroke events in Moyamoya patients: A systematic review and meta-analysis of diagnostic test accuracy

Jeremiah Hilkiah Wijaya; Miguel Quintero Consuegra; Daniela A Perez-Chadid; Aafreen Azmi; Juan Pablo Avila Madrigal; Shane Shahrestani; Sofia Ramirez-Guerrero; Anil Nanda; Abdelrahman Ramadan Elashry; Nestor R Gonzalez

doi:10.1016/j.jstrokecerebrovasdis.2026.108542

The role of artificial intelligence in estimating stroke events in Moyamoya patients: A systematic review and meta-analysis of diagnostic test accuracy

J Stroke Cerebrovasc Dis. 2026 Jan 2;35(3):108542. doi: 10.1016/j.jstrokecerebrovasdis.2026.108542. Online ahead of print.

Affiliations

¹ School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia. Electronic address: jwij0023@student.monash.edu.
² Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
³ Department of Neurosurgery, Rutgers New Jersey Medical School, Rutgers University, NJ, USA.
⁴ Department of Neurosurgery, Robert Wood Johnson University Hospital, Robert Wood Johnson Medical School, Rutgers University, NJ, USA.
⁵ School of Medicine and Health Sciences, Universidad del Rosario, Bogotá, Colombia.
⁶ College of Medicine, University of Sharjah, Sharjah, UAE.

PMID: 41485586
DOI: 10.1016/j.jstrokecerebrovasdis.2026.108542

Abstract

Introduction: Moyamoya disease (MMD) and syndrome (MMS) are rare cerebrovascular arteriopathies marked by progressive internal carotid stenosis, fragile collateral networks, and a five-year stroke risk near 10% despite optimal care. Artificial-intelligence (AI) models integrating angiographic, perfusion, and clinical data show promise for risk stratification, but their diagnostic accuracy and clinical readiness remain uncertain.

Method: We conducted a systematic review of AI algorithms for predicting ischemic or hemorrhagic stroke events in angiographically or magnetic resonance imaging (MRI)-confirmed MMD/MMS. PubMed, EMBASE, and Scopus were searched through September 3, 2025, for English-language studies employing machine-learning, deep-learning, or radiomics models. We extracted sensitivity, specificity, and area under the curve (AUC) metrics and assessed study quality with Radiomics Quality Score and CLEAR checklists. Pooled estimates and summary receiver-operating characteristic curves were generated; decision-curve analysis evaluated clinical net benefit.

Results: Seven retrospective cohorts (n = 4,795) met inclusion criteria. The pooled sensitivity was 0.65 (95% CI 0.50-0.79) and specificity 0.85 (95% CI 0.82-0.89). The summary AUC was 0.85. Decision-curve analysis demonstrated that AI predictions improved net benefit over "treat-all" or "treat-none" strategies across relevant risk thresholds. Tree-based classifiers (XGBoost, random forest) showed more stable external performance than deep-learning networks. Explainability tools enhanced model interpretability.

Conclusion: AI models achieve moderate-to-high accuracy for stroke prediction in MMD/MMS and offer potential for individualized risk stratification. However, small single-center datasets, heterogeneous imaging protocols, and opaque modeling limit clinical adoption. Prospective multicenter validation, standardized data pipelines, and robust explainability frameworks are essential for integrating AI into routine neurovascular care.

Keywords: Artificial intelligence; Diagnostic accuracy; Moyamoya disease; Stroke prediction.