Abstract：
It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.
摘要：
使用机器学习（ML）算法可以识别未破裂的颅内动脉瘤（UIA）。这可能是一个拯救生命的策略，尤其是高危人群。为了更好地理解ML算法在实践中的重要性和有效性，我们进行了系统评价和荟萃分析以预测脑动脉瘤破裂风险.PubMed,Scopus,WebofScience,直到2023年3月20日，Embase才被无限制地搜索。合格标准包括在DSA确认的脑动脉瘤患者中使用ML方法的研究，CTA,或MRI。在包括的35项研究中，33人是队列，11使用数字减影血管造影（DSA）作为参考成像模式。大脑中动脉（MCA）和大脑前动脉（ACA）是动脉瘤血管受累最常见的位置-51％和40％，分别。在48％的研究中，动脉瘤的形态是囊状的。37项研究中有10项(27%)使用了深度学习技术，如CNN和ANN。对17项研究进行了荟萃分析：敏感性为0.83（95％置信区间（CI），0.77-0.88）；特异性为0.83（95％CI，0.75-0.88）；阳性DLR为4.81（95％CI，3.29-7.02），阴性DLR为0.20（95％CI，0.14-0.29）；诊断评分为3.17（95％CI，2.55-3.78）；比值比为23.69（95％CI，12.75-44.01）。ML算法可以有效预测脑动脉瘤破裂的风险，具有良好的准确性，灵敏度,和特异性。然而,需要进一步的研究来提高其在预测IA破裂状态方面的诊断能力。
