Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review.

IF 2.4 3区医学 Q2 CLINICAL NEUROLOGY Clinical Neuroradiology Pub Date : 2024-11-15 DOI:10.1007/s00062-024-01474-4

Karan Daga, Siddharth Agarwal, Zaeem Moti, Matthew B K Lee, Munaib Din, David Wood, Marc Modat, Thomas C Booth

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Abstract

Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk.

Methods: MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023. Studies incorporating any machine learning algorithm to predict the risk of rupture of an intracranial aneurysm were included. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). PROSPERO registration: CRD42023452509.

Results: Out of 10,307 records screened, 20 studies met the eligibility criteria for this review incorporating a total of 20,286 aneurysm cases. The machine learning models gave a 0.66-0.90 range for performance accuracy. The models were compared to current clinical standards in six studies and gave mixed results. Most studies posed high or unclear risks of bias and concerns for applicability, limiting the inferences that can be drawn from them. There was insufficient homogenous data for a meta-analysis.

Conclusions: Machine learning can be applied to predict the risk of rupture for intracranial aneurysms. However, the evidence does not comprehensively demonstrate superiority to existing practice, limiting its role as a clinical adjunct. Further prospective multicentre studies of recent machine learning tools are needed to prove clinical validation before they are implemented in the clinic.

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预测颅内动脉瘤破裂风险的机器学习算法：系统性综述。

目的：蛛网膜下腔出血是颅内动脉瘤破裂的潜在致命后果，但很难预测动脉瘤是否会破裂。颅内动脉瘤的预防性治疗也存在风险，因此识别易破裂的动脉瘤具有重要的临床意义。本系统综述旨在评估预测颅内动脉瘤破裂风险的机器学习算法的性能：方法：检索 MEDLINE、Embase、Cochrane Library 和 Web of Science，检索期至 2023 年 12 月。纳入了采用任何机器学习算法预测颅内动脉瘤破裂风险的研究。偏倚风险采用预测模型偏倚风险评估工具（PROBAST）进行评估。PROSPERO 注册：CRD42023452509.Results:在筛选出的 10,307 条记录中，有 20 项研究符合本综述的资格标准，共纳入 20,286 例动脉瘤病例。机器学习模型的准确度在 0.66-0.90 之间。有六项研究将模型与现行临床标准进行了比较，结果不一。大多数研究都存在较高或不明确的偏倚风险和适用性问题，从而限制了从中得出的推论。没有足够的同质数据进行荟萃分析：结论：机器学习可用于预测颅内动脉瘤破裂的风险。结论：机器学习可用于预测颅内动脉瘤的破裂风险，但相关证据并未全面证明其优于现有实践，从而限制了其作为临床辅助手段的作用。需要对最新的机器学习工具进行进一步的前瞻性多中心研究，以证明其临床有效性，然后再将其应用于临床。

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来源期刊

Clinical Neuroradiology CLINICAL NEUROLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

CiteScore

5.00

自引率

3.60%

发文量

106

审稿时长

>12 weeks

期刊介绍： Clinical Neuroradiology provides current information, original contributions, and reviews in the field of neuroradiology. An interdisciplinary approach is accomplished by diagnostic and therapeutic contributions related to associated subjects. The international coverage and relevance of the journal is underlined by its being the official journal of the German, Swiss, and Austrian Societies of Neuroradiology.

期刊最新文献

Mismatch Vs No Mismatch in Large Core-A Matter of Definition. Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review.