Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.
Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.
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使用人工智能检测脑动脉瘤：系统综述和荟萃分析。
DOI：
10.1136/jnis-2022-019456
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发表时间：
2023-03
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
脑动脉瘤破裂引起的蛛网膜下腔出血是发病和死亡的主要原因。在自动化系统的帮助下，早期动脉瘤识别可以改善患者的治疗效果。因此，我们对人工智能 (AI) 算法使用 CT、MRI 或 DSA 检测脑动脉瘤的诊断准确性进行了系统回顾和荟萃分析。 MEDLINE、Embase、Cochrane Library 和 Web of Science 的检索截止日期为 2021 年 8 月。资格标准包括使用全自动算法通过 MRI、CT 或 DSA 检测脑动脉瘤的研究。根据系统评价和荟萃分析的首选报告项目：诊断测试准确性 (PRISMA-DTA)，文章使用诊断准确性研究质量评估 2 (QUADAS-2) 进行评估。荟萃分析包括双变量随机效应模型，以确定汇总敏感性、特异性和受试者工作特征曲线下面积 (ROC-AUC)。普洛斯彼罗：CRD42021278454。纳入 43 项研究，其中 41/43 (95%) 为回顾性研究。 34/43 (79%) 使用人工智能作为独立工具，而 9/43 (21%) 使用人工智能辅助读者。 23/43 (53%) 使用深度学习。大多数研究存在较高的偏倚风险和适用性问题，限制了结论。独立 AI 荟萃分析中的六项研究给出（汇总）91.2%（95% CI 82.2% 至 95.8%）的敏感性； 16.5%（95% CI 9.4% 至 27.1%）假阳性率（1-特异性）； 0.936 ROC-AUC。五项读者辅助人工智能研究给出（汇总）90.3% (95% CI 88.0% – 92.2%) 的敏感性；假阳性率 7.9%（95% CI 3.5% 至 16.8%）； 0.910 ROC-AUC。人工智能有潜力支持临床医生检测脑动脉瘤。由于偏倚风险高且普遍性差，解释受到限制。需要多中心、前瞻性研究来评估临床实践中的人工智能。
Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence (AI) algorithms in detecting cerebral aneurysms using CT, MRI or DSA was performed. MEDLINE, Embase, Cochrane Library and Web of Science were searched until August 2021. Eligibility criteria included studies using fully automated algorithms to detect cerebral aneurysms using MRI, CT or DSA. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy (PRISMA-DTA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis included a bivariate random-effect model to determine pooled sensitivity, specificity, and area under the receiver operator characteristic curve (ROC-AUC). PROSPERO: CRD42021278454. 43 studies were included, and 41/43 (95%) were retrospective. 34/43 (79%) used AI as a standalone tool, while 9/43 (21%) used AI assisting a reader. 23/43 (53%) used deep learning. Most studies had high bias risk and applicability concerns, limiting conclusions. Six studies in the standalone AI meta-analysis gave (pooled) 91.2% (95% CI 82.2% to 95.8%) sensitivity; 16.5% (95% CI 9.4% to 27.1%) false-positive rate (1-specificity); 0.936 ROC-AUC. Five reader-assistive AI studies gave (pooled) 90.3% (95% CI 88.0% – 92.2%) sensitivity; 7.9% (95% CI 3.5% to 16.8%) false-positive rate; 0.910 ROC-AUC. AI has the potential to support clinicians in detecting cerebral aneurysms. Interpretation is limited due to high risk of bias and poor generalizability. Multicenter, prospective studies are required to assess AI in clinical practice.
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