(untitled) (Emergent: Journal of Educational Discoveries and Lifelong Learning (EJEDL), 2025)
Stroke is a leading cause of death and long-term disability worldwide, and early detection is crucial for effective treatment and improved patient outcomes. Neuroimaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI), are essential for identifying ischemic and hemorrhagic strokes. However, manual interpretation of neuroimages can be time-consuming, subjective, and dependent on radiologist expertise. Artificial intelligence (AI) and deep learning methods, particularly convolutional neural networks (CNNs), provide automated, accurate, and rapid analysis of neuroimaging data, enabling early detection, classification, and localization of stroke lesions. This paper reviews current AI methodologies for stroke detection using CT and MRI, discusses challenges such as image variability, limited annotated datasets, and model interpretability, and highlights the potential of AI systems to enhance diagnostic accuracy, optimize clinical workflows, and improve patient outcomes.