Implications of Artificial Intelligence in Stroke Intervention and Care

Abstract

Artificial intelligence (AI) technology is expanding at a rapid pace, offering means of improving the precision of judgments made by medical professionals. AI-driven machine learning (ML) facilitates rapid and effective data processing for diagnosis and treatment of different diseases including stroke. This technology has vastly improved the patient classification based on their predicted stroke outcome. It helps in quicker decision-making, improves diagnosis precision, and enhances patient care. ML techniques have occasionally been applied extensively to address complex issues related to stroke such as the prediction of stroke prevalence at an early stage. The ability of deep learning (DL) algorithms, a crucial element of AI, is becoming popular in stroke imaging analysis because it automatically extracts features without requiring domain expertise. In the preclinical setup for stroke studies, ML/DL models are commendably used for the detection of vascular thrombi, stroke core, and penumbra size, to identify artery occlusion, compute perfusion maps, detect intracranial hemorrhage (ICH), prediction of infarct, assessing the severity of hemorrhagic transformation, and forecasting patient outcomes. The robust automatic data processing, excellent generalization, self-learning, and precise decision-making abilities of such models have contributed immensely to the advancement of stroke therapy. In the preclinical setup, the time-investing behavioral studies of the animals are also effectively analyzed by AI based algorithms. Understanding the algorithms and models based on AI is yet to be simplified for its application in stroke therapy in present clinical settings, thus, in the present review attempts have been made to present it in a simplified manner to facilitate translation.