AI-BASED EARLY DETECTION OF STROKE

Abstract

Stroke is a leading cause of disability and mortality worldwide, and early detection is critical for effective intervention and improved patient outcomes. Traditional diagnostic methods, including clinical evaluation and imaging, can be time-consuming and may delay timely treatment. Artificial intelligence (AI) and machine learning techniques provide automated, rapid, and accurate solutions for early stroke detection by analyzing clinical data, neuroimaging, and physiological signals. This paper reviews current AI-based methodologies for stroke prediction and early diagnosis, focusing on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. Challenges such as data heterogeneity, noise, and model interpretability are discussed. The study highlights the potential of AI systems to enhance early diagnosis, guide therapeutic decisions, and improve patient care in acute stroke management.