The Use of Simulation and Deep Learning Models in the Endovascular Treatment of Ruptured Intracranial Aneurysms: A Case Report

Abstract

Abstract Introduction The current paper presents an examination of the emerging role of deep learning-based simulation software in enhancing preprocedural planning for intracranial aneurysm treatment using flow diverters. Intracranial aneurysms pose significant risk due to their potential rupture leading to life-threatening subarachnoid hemorrhage. Innovative endovascular treatment options like flow diverters, which redirect blood flow and promote healing, are gaining attention. The role of simulation software in optimizing these procedures is becoming increasingly crucial. Case presentation This study involves a 47-year-old female patient diagnosed with an intracranial aneurysm. Through diagnostic angiography and 3D rotational angiography imaging, the complex aneurysm anatomy was determined and the need for flow diverter placement ascertained. The Sim&Size™ software was used to simulate the size and placement of the flow diverter, based on the patient’s specific vascular anatomy. The procedure, including the placement of the flow diverter as per the simulation, was successful. Conclusion The Sim&Size™ simulation software significantly contributes to the enhancement of intracranial aneurysm treatment planning. By providing patient-specific simulations, it improves procedural precision and reduces the risk of complications, thus potentially optimizing patient outcomes. However, the quality of the simulation is contingent on the accuracy of the input data, and it does not account for physiological dynamics. Despite these limitations, this tool represents a promising development in neurointerventional practice.