The role of artificial intelligence in estimating stroke events in Moyamoya patients: A systematic review and meta-analysis of diagnostic test accuracy
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.
Authors
- Aafreen Azmi
- Juan Pablo Ávila Madrigal
- Jeremiah Hilkiah Wijaya (ORCID: https://orcid.org/0000-0001-8771-2163)
- Shane Shahrestani (ORCID: https://orcid.org/0000-0001-7561-4590)
- Nestor R. Gonzalez
- Sofía Ramírez-Guerrero
- Abdelrahman Ramadan Elashry
- Miguel Quintero Consuegra
- Daniela A Perez-Chadid
- Anil Nanda
Institutions
- Rutgers, The State University of New Jersey (US)
- Cedars-Sinai Medical Center (US)
- Robert Wood Johnson University Hospital (US)
- University of Sharjah (AE)
- Monash Health (AU)
- Monash University (AU)
- Universidad del Rosario (CO)
Publication Details
- Journal
- Journal of Stroke and Cerebrovascular Diseases
- Published
- 2026-01-02
- DOI
- https://doi.org/10.1016/j.jstrokecerebrovasdis.2026.108542
- Primary Topic
- Moyamoya disease diagnosis and treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00