Prediction of Cerebral Aneurysm Rupture Risk by Machine Learning Algorithms: A Systematic Review and Meta-Analysis of 18,670 Participants Publisher Pubmed



Habibi MA¹ ; Fakhfouri A² ; Mirjani MS³ ; Razavi A⁴ ; Mortezaei A⁵ ; Soleimani Y² ; Lotfi S² ; Arabi S² ; Heidaresfahani L² ; Sadeghi S² ; Minaee P² ; Eazi SM² ; Rashidi F⁶ ; Shafizadeh M¹ Show All Authors Show Affiliations
Source: Neurosurgical Review Published:2024

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. © 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.