Advancing aneurysm management: the potential of AI and machine learning in enhancing safety and predictive accuracy

Cerebral aneurysm rupture, the predominant cause of non-traumatic subarachnoid hemorrhage, underscores the need for effective treatment and early detection methods. A study in Neurosurgical Review compared microsurgical clipping to endovascular therapy in 130 patients with middle cerebral artery (MCA) aneurysms, finding significantly fewer serious adverse events (SAEs) and neurological complications in the endovascular group. This suggests endovascular therapy's superiority in safety and reducing complications for MCA aneurysm patients. Furthermore, a systematic review and meta-analysis assessed the diagnostic accuracy of AI algorithms in detecting cerebral aneurysms, revealing a high sensitivity but notable false-positive rates, indicating AI's potential while highlighting the need for further validation. Machine learning algorithms also showed promise in predicting cerebral aneurysm rupture risk, demonstrating reasonable sensitivity and specificity. Additionally, AI-based radiomics models are advancing rapidly, offering enhanced predictive accuracy and personalized treatment planning by analyzing imaging data to identify features indicative of aneurysm conditions. Collectively, these findings emphasize the advantages of endovascular therapy for MCA aneurysms and the emerging role of AI and machine learning in improving early detection and personalized management of cerebral aneurysms.