Revolutionizing Aneurysm detection: The role of artificial intelligence in reducing rupture rates.

Abstract

Cerebral aneurysms, affecting 2-5% of the global population, are often asymptomatic and commonly located within the Circle of Willis. A recent study in Neurosurgical Review highlights a significant reduction in the annual rupture rates of unruptured cerebral aneurysms (UCAs) in Japan from 2003 to 2018. By analyzing age-adjusted mortality rates of subarachnoid hemorrhage (SAH) and the number of treated ruptured cerebral aneurysms (RCAs), researchers found a substantial decrease in rupture rates-from 1.44 to 0.87% and from 0.92 to 0.76%, respectively (p < 0.001). This 88% reduction was largely attributed to improved hypertension management. Recent advancements in artificial intelligence (AI) and machine learning (ML) further support these findings. The RAPID Aneurysm software demonstrated high accuracy in detecting cerebral aneurysms on CT Angiography (CTA), while ML algorithms showed promise in predicting aneurysm rupture risk. A meta-analysis indicated that ML models could achieve 83% sensitivity and specificity in rupture prediction. Additionally, deep learning techniques, such as the PointNet + + architecture, achieved an AUC of 0.85 in rupture risk prediction. These technological advancements in AI and ML are poised to enhance early detection and risk management, potentially contributing to the observed reduction in UCA rupture rates and improving patient outcomes.