Advancing Stroke Diagnosis: A Comprehensive Review of Artificial Intelligence in Detecting Early Ischemic Changes on...

Detecting Early Ischemic Changes (EIC) on noncontrast computed tomography (NCCT) is essential in patient selection for reperfusion therapy in acute ischemic stroke (AIS). However, identifying these subtle changes remains challenging due to their variable presentation, dependence on reader expertise, and significant interobserver variability. Therefore, an objective method for identifying and quantifying early ischemic brain damage is needed to assist clinicians, particularly in resource-limited settings. Recent advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have opened new opportunities to enhance stroke diagnosis by enabling fast, consistent, and accurate analysis of NCCT images. This review summarizes current AI applications in detecting EIC on NCCT images, focusing on two major developments: (1) the automatic calculation of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which facilitates automated tracing of regions of interest (ROIs) and quantification of hypoattenuation to objectively assess ischemic damage, and (2) DL-based EIC detection approaches, supported by large-scale datasets. We highlight the potential of these innovations to complement clinical expertise, streamline workflows, and improve patient outcomes. We discuss the methodologies, performance metrics, and limitations of existing AI models. By synthesizing the latest research, this paper explores AI's transformative role in AIS management and outlines future directions for innovation in this rapidly evolving field.