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.
摘要：
脑动脉瘤,影响了全球2-5%的人口，通常无症状，通常位于威利斯圈内。《神经外科评论》最近的一项研究强调，从2003年到2018年，日本未破裂脑动脉瘤（UCA）的年破裂率显着降低。通过分析蛛网膜下腔出血（SAH）的年龄调整死亡率和治疗的破裂脑动脉瘤（RCA）的数量，研究人员发现，破裂率从1.44降至0.87%，从0.92降至0.76%，分别(p<0.001)。88％的减少主要归因于高血压管理的改善。人工智能（AI）和机器学习（ML）的最新进展进一步支持了这些发现。RAPID动脉瘤软件在CT血管造影（CTA）上检测脑动脉瘤时表现出很高的准确性，而ML算法在预测动脉瘤破裂风险方面显示出希望。荟萃分析表明，ML模型在破裂预测中可以达到83％的敏感性和特异性。此外,深度学习技术，例如PointNet++架构，破裂风险预测的AUC为0.85。人工智能和机器学习的这些技术进步有望加强早期检测和风险管理。可能有助于观察到的UCA破裂率降低和改善患者预后。
