Our focus is on enhancing patient management in mechanical thrombectomy by improving image quality, predicting complications, and optimizing treatment planning to improve outcomes and streamline workflows.

Artificial Intelligence (AI) is revolutionizing interventional neuroradiology by enhancing diagnostic accuracy, improving procedural efficiency, and optimizing patient outcomes. AI algorithms are capable of analyzing complex imaging data, such as CT and MRI scans, to assist radiologists in making more informed decisions. Additionally, machine learning techniques can help predict complications and personalize treatment plans.

In this thesis, we aim to enhance patient management related to the mechanical thrombectomy procedure. Our focus is on three key aspects: improving image quality, predicting potential complications more accurately, and optimizing treatment planning and decision-making. By addressing these areas, we seek to improve overall patient outcomes and streamline the procedural workflow.