{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T20:36:05Z","timestamp":1784666165220,"version":"3.55.0"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>Moyamoya disease (MMD), a chronic, progressive cerebrovascular disorder of unknown etiology, presents significant diagnostic and therapeutic challenges in clinical practice. Conventional diagnostic methods rely on physicians\u2019 experience and have limitations in disease prediction, risk assessment, and treatment decisions. The advancement of artificial intelligence (AI) technologies has created new opportunities for research on MMD. This review summarizes recent advances in AI applications for MMD, including diagnosis, risk factor analysis, treatment planning, outcome evaluation, and basic research. Additionally, this review critically examines the limitations of current research on MMD and explores potential future directions, aiming to offer valuable insights and guidance on MMD.<\/jats:p>","DOI":"10.3389\/fneur.2025.1581338","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T04:11:06Z","timestamp":1747368666000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Research progress of artificial intelligence in moyamoya 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