Aneurysmal subarachnoid hemorrhage (SAH) is one of the critical diseases that severely threaten human health, with a clinical mortality rate reaching as high as 30%. Early diagnosis and intervention before rupture are considered key to improving the prognosis of aneurysmal SAH. With the widespread clinical application of non-invasive cerebrovascular imaging techniques, such as CTA and MRA, the detection rate of unruptured intracranial aneurysms (UIAs) has significantly increased. However, addressing the growing demand for clinical cerebrovascular imaging diagnostics raises the challenge of improving diagnostic accuracy while alleviating the workload of diagnostic physicians. Furthermore, considering that not all detected UIAs will rupture, it is crucial to accurately identify high-risk aneurysms prone to rupture to avoid unnecessary overtreatment, which could lead to significant socioeconomic burdens and iatrogenic harm to patients.To meet this clinical need, researchers have developed an artificial intelligence (AI) algorithm to create software capable of automatically identifying intracranial aneurysms based on non-invasive vascular imaging data, enabling accurate diagnosis of aneurysms. To evaluate the clinical utility of this AI algorithm, a prospective, multicenter, registry study was proposed. Through long-term standardized and uniform non-invasive imaging follow-up, individualized imaging analysis profiles will be established. By correlating these profiles with aneurysm outcome events (growth or rupture), imaging features capable of accurately predicting aneurysm growth and rupture will be identified and analyzed. This approach is expected to enhance the accuracy of UIA diagnosis and enable risk stratification for unruptured intracranial aneurysms through the utilization of relevant data.