Machine Learning Algorithms for Rupture Risk Assessment of Intracranial Aneurysms: A Diagnostic Meta-Analysis

• Published in: World neurosurgery Vol. 165; pp. e137 - e147
• Main Authors: Shu, Zhang, Chen, Song, Wang, Wei, Qiu, Yufa, Yu, Ying, Lyu, Nan, Wang, Chi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2022
• Subjects: Algorithms; Cerebral aneurysms; Humans; Intracranial Aneurysm - diagnosis; Intracranial aneurysms; Machine Learning; Meta-analysis; Risk Assessment; ROC Curve; Rupture; Intracranial aneurysms; CI; DOR; QUADAS-2; Rupture; MeSH; PRISMA; SVM; Meta-analysis; CTA; DSA; NLR; Machine learning; PLR; IA; Cerebral aneurysms; CA
• ISSN: 1878-8750; 1878-8769; 1878-8769
• DOI: 10.1016/j.wneu.2022.05.117

Several machine learning algorithms have been increasingly applied to predict the rupture risk of intracranial aneurysms. We performed the present diagnostic meta-analysis to comprehensively evaluate the diagnostic value of machine learning algorithms for assessing the rupture risk of intracranial aneurysms. We systematically searched 3 electronic databases, including Medline (via PubMed), the Cochrane Register of Controlled Trials (via Ovid), and Embase (via Elsevier), to retrieve eligible studies from the databases’ inception through March 2021. The latest update was performed in June 2021. StataMP, version 14, was used to estimate all pooled diagnostic values. A total of 4 studies involving 6 reports were considered to meet the inclusion criteria. Our diagnostic meta-analysis generated the following pooled diagnostic values: sensitivity, 0.84 (95% confidence interval [CI], 0.75–0.90); specificity, 0.78 (95% CI, 0.68–0.85); positive likelihood ratio, 3.8 (95% CI, 2.4–5.9); negative likelihood ratio, 0.21 (95% CI, 0.12–0.35), diagnostic odd ratio, 18 (95% CI, 7–46), and area under the summary receiver operating characteristic curve, 0.88 (95% CI, 0.85–0.90). Our findings have demonstrated that the diagnostic performance of machine learning algorithms for the rupture risk assessment of AIs is excellent. Considering that the negative effects resulted from the limited number of eligible studies, we suggest developing more well-designed studies with larger sample sizes to validate our findings.