Revolutionizing acute ischemic stroke care: a systematic review of machine learning and deep learning applications in diagnosis and outcome prediction

Abstract Acute ischemic stroke (AIS) represents a major global health burden, with incidence projected to reach 89.32 per 100,000 people by 2030. This systematic review examines how artificial intelligence (AI), particularly machine learning and deep learning, can enhance AIS care through improved d...

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Autori principali:	Subasini Ramesh, Snekhalatha Umapathy
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	SpringerOpen 2026-02-01
Serie:	The Egyptian Journal of Neurology, Psychiatry and Neurosurgery
Soggetti:	Acute ischemic stroke (AIS) Diagnosis Ethical challenges Lesion segmentation Machine learning (ML) Outcome prediction
Accesso online:	https://doi.org/10.1186/s41983-026-01091-7
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