A Machine Learning Approach to Cerebral Edema Evaluation in Ischemic Stroke

Abstract: In the discipline of informatics, artificial intelligence (AI) uses algorithms to process data and constantly refines its reasoning. AI, which began in the 1950s and has since evolved into "machine learning algorithms," encompasses Deep Learning for pattern recognition in medical images and Machine Learning for data analysis. With the use of augmented reality and virtual reality, AI has the potential to dramatically improve healthcare, especially for radiologists working in diagnostic imaging and interventional radiology. Working in diagnostic medicine and interventional radiologists. AI applications in the field of interventional radiology include patient selection, treatment planning, and training. Thorough research and validation are crucial to successfully integrating AI to improve patient care. This study examines the prognostic value of haemorrhagic transformation (HT) in acute ischemic stroke inferred from MRI-derived permeability measurements using MR perfusion images that was done.

Keywords: Artificial Intelligence (AI), Informatics, Data Processing, Hemorrhagic Transformation (HT), Acute Ischemic Stroke, Patient Outcomes, Predictive Model.