What can we learn from machine learning studies on flow diverter aneurysm embolization? A systematic review.

IF 4.5 1区 医学 Q1 NEUROIMAGING
Esref Alperen Bayraktar, Jonathan Cortese, Mohamed Sobhi Jabal, Sherief Ghozy, Atakan Orscelik, Cem Bilgin, Ramanathan Kadirvel, Waleed Brinjikji, David F Kallmes
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引用次数: 0

Abstract

Background: As the use of flow diverters has expanded in recent years, predicting successful outcomes has become more challenging for certain aneurysms.

Objective: To provide neurointerventionalists with an understanding of the available machine learning algorithms for predicting the success of flow diverters in occluding aneurysms.

Methods: This study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the four major medical databases (PubMed, Embase, Scopus, Web of Science) were screened. The study included original research articles that evaluated the predictive abilities of various machine learning algorithms for determining the success of flow diverters in achieving aneurysm occlusion.

Results: Five studies out of 217 were included based on our criteria. The included studies used various variables (patient demographics, aneurysm and parent artery characteristics, flow diverter and hemodynamic-related features, and angiographic parametric imaging) to predict flow diverter treatment outcomes. The machine learning algorithms used, along with their respective accuracy rates, were as follows: logistic regression (61% and 85%), support vector machine (88%), Gaussian support vector machine (90%), linear support vector machine (85%), decision tree (80%), random forest (87%), k-nearest neighbors (83% and 85%), XGBoost (87%), CatBoost (86%), deep neural networks (77.9%), and recurrent neural networks (74%).Two studies trained the machine learning models with both all features and the most significant features. Both studies observed that the accuracy of machine learning models decreased by removing the insignificant features.

Conclusion: The current literature indicates that machine learning algorithms can be trained to predict the success of flow diverters with an accuracy of up to 90%.

我们能从分流动脉瘤栓塞的机器学习研究中学到什么?系统回顾。
背景:近年来,随着流量分流器的应用越来越广泛,预测某些动脉瘤的成功预后变得越来越具有挑战性。目的:为神经介入医师提供可用的机器学习算法,以预测血流分流器在闭塞动脉瘤中的成功应用。方法:本研究遵循PRISMA (Preferred Reporting Items for Systematic Reviews and meta - analysis)指南,筛选四大医学数据库(PubMed、Embase、Scopus、Web of Science)。该研究包括原创研究文章,评估了各种机器学习算法的预测能力,以确定分流器在实现动脉瘤闭塞方面的成功。结果:根据我们的标准,217项研究中有5项被纳入。纳入的研究使用了各种变量(患者人口统计学、动脉瘤和载动脉特征、分流器和血流动力学相关特征以及血管造影参数成像)来预测分流器治疗结果。所使用的机器学习算法及其各自的准确率如下:逻辑回归(61%和85%)、支持向量机(88%)、高斯支持向量机(90%)、线性支持向量机(85%)、决策树(80%)、随机森林(87%)、k近邻(83%和85%)、XGBoost(87%)、CatBoost(86%)、深度神经网络(77.9%)和循环神经网络(74%)。两项研究用所有特征和最重要的特征训练机器学习模型。两项研究都观察到,通过去除不重要的特征,机器学习模型的准确性降低了。结论:目前的文献表明,机器学习算法可以被训练来预测分流器的成功率,准确率高达90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.50
自引率
14.60%
发文量
291
审稿时长
4-8 weeks
期刊介绍: The Journal of NeuroInterventional Surgery (JNIS) is a leading peer review journal for scientific research and literature pertaining to the field of neurointerventional surgery. The journal launch follows growing professional interest in neurointerventional techniques for the treatment of a range of neurological and vascular problems including stroke, aneurysms, brain tumors, and spinal compression.The journal is owned by SNIS and is also the official journal of the Interventional Chapter of the Australian and New Zealand Society of Neuroradiology (ANZSNR), the Canadian Interventional Neuro Group, the Hong Kong Neurological Society (HKNS) and the Neuroradiological Society of Taiwan.
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