I. Sahin, A.S. Favate, P. R. Garigapuram, S. Katore
New York University, New York, United States
Keywords: Intracranial Aneurysms, Computational Fluid Dynamics, Hemodynamics, Risk of Rupture, Artificial Intelligence, Machine Learning
The risk determination in Intracranial Aneurysms (IA) conventionally relies on invasive procedures. These procedures are arduous for the patient and unnecessary due to uncertainty surrounding the initial diagnosis. This research addresses the shortcomings of traditional invasive procedures and emphasizes the significance of non-invasive practices. The proposed technology offers an automated, cloud-based software tool that harnesses imaging modalities like CT and MRI scans to provide non-invasive, patient-specific risk assessments for clinical implementation. By harnessing the power of AI/ML algorithms to handle the results obtained from dynamic simulation for hemodynamic parameters, the morphological features, and the patient history, a comprehensive analysis is obtained, which provides insights into the risk of rupture, progression, and growth of aneurysms, and actionable insights into the blood flow patterns and velocities indicating critical locations for tear, which would lead to an enhanced treatment plan for surgeries and patient handling. The solution will not only enhance treatment planning but will result in critical insights being delivered early for a prompt response and the development of a patient-centric solution, ultimately enabling physicians with more control over invasive procedures, and aid in monitoring the patient both pre and post-treatment, all the while providing a streamlined workflow reducing interdepartmental stress.