Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging

Abstract

Artificial intelligence is revolutionizing the interpretation of medical images, helping healthcare professionals save time on magnetic scans, CT scans and X-rays. Stroke is a global health problem, and ischemic stroke is one of the leading causes of death and disability in humans. Symptoms of an ischemic stroke appear suddenly and worsen within minutes, as most ischemic strokes occur suddenly, progress rapidly, and lead to the death of brain tissue within minutes or hours. This is why early detection of stroke is essential and remains a challenge for neurophysicists. Neurophysicists routinely use a variety of detection techniques to detect, assess and evaluate the ex-tent of premature ischemic changes in acute stroke brain imaging. Although several effective techniques exist, these methods have limitations due to unenhanced CT scans and invasive techniques. This study aims to demonstrate the limitations of certain methods, to determine detection methods by comparing the detection performance of automated and human brains. Stroke was evaluated through a literature review of recent studies.This article highlights comparative studies of different artificial intelligence (AI) techniques using medical imaging and allows the authors to orient themselves within these comparative studies, thus projecting themselves into the challenges facing artificial intelligence.