Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
King’s College London（伦敦国王学院）；Guy’s and St. Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；University College London Hospital NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；King’s College Hospital（国王学院医院）
Karan Daga, Siddharth Agarwal, Zaeem Moti, Matthew BK Lee, Munaib Din, David Wood, Marc Modat, Thomas C Booth
目的：蛛网膜下腔出血是颅内动脉瘤破裂的潜在致命后果，但难以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，因此识别易破裂动脉瘤具有重要临床意义。本系统综述旨在评估机器学习算法预测颅内动脉瘤破裂风险的性能。方法：检索MEDLINE、Embase、Cochrane Library和Web of Science数据库至2023年12月，纳入所有采用机器学习算法预测颅内动脉瘤破裂风险的研究，使用预测模型偏倚风险评估工具（PROBAST）评估偏倚风险，PROSPERO注册号为CRD42023452509。结果：在筛选的10307条记录中，20项研究符合纳入标准，共涉及20286例动脉瘤病例；机器学习模型的性能准确率范围为0.66-0.90；6项研究将模型与当前临床标准比较，结果喜忧参半；多数研究存在高偏倚风险或适用性问题，限制了结论的推断；缺乏足够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风险，但现有证据未全面证明其优于现有实践，限制了其作为临床辅助工具的作用；在临床应用前，需进一步开展前瞻性多中心研究验证近期机器学习工具的临床有效性。
英文摘要：
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.
Uppsala University（乌普萨拉大学）；Cornell University（康奈尔大学）；Cornell Tech（康奈尔科技学院）；Weill Cornell Medicine（威尔康奈尔医学院）；Radiological Society of North America（北美放射学会）；Taipei Medical University Hospital（台北医学大学附属医院）；UC San Diego（加州大学圣地亚哥分校）；Prince Sultan Military Medical City（苏丹王子军事医疗城）；Liverpool Hospital（利物浦医院）；Clinical Center of the University of Sarajevo（萨拉热窝大学临床中心）；Memorial University of Newfoundland（纽芬兰纪念大学）；Fleni（弗莱尼医院）；University of California San Francisco（加州大学旧金山分校）；Koç University School of Medicine（科奇大学医学院）；Intermed IUHW Hospital（国际医疗福祉大学医院）；Hacettepe University（哈塞特佩大学）；Duke University（杜克大学）；China Medical University Hospital（中国医药大学附属医院）
Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
Rethinking Intracranial Aneurysm Vessel Segmentation: A Perspective from Computational Fluid Dynamics Applications
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Department of Civil and Environmental Engineering, Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Huashan Hospital, Fudan University（复旦大学华山医院）
Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院高级医学计算与分析中心）；Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院戈登医学影像中心）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院放射科）
Weakly Supervised Intracranial Aneurysm Detection and Segmentation in MR angiography via Multi-task UNet with Vesselness Prior
Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada（计算机科学与软件工程系，康科迪亚大学）；NeuroRx Research, Montréal, Canada（NeuroRx研究公司）；Department of Electrical and Computer Engineering, Concordia University, Montréal, Canada（电气与计算机工程系，康科迪亚大学）
Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Brigham and Women’s Hospital（布列根和妇女医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Washing University in St. Louis（圣路易斯华盛顿大学）；First Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第一医院）
3D convolutional neural network for abdominal aortic aneurysm segmentation
Vicomtech Foundation（维克姆泰克基金会）；Biodonostia Health Research Institute（比奥多诺斯蒂亚健康研究所）；Universitat Pompeu Fabra（庞培法布拉大学）；Donostia University Hospital（多诺斯蒂亚大学医院）；ICREA（加泰罗尼亚高等研究院）
Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models
organization= Department of Applied Mathematics, Technical Medical Centre, University of Twente , addressline= Drienerlolaan 5 , city= Enschede , postcode= 7522 NB , country= The Netherlands；organization= Cardiovascular Health Technology Centre, University of Twente , addressline= Drienerlolaan 5 , city= Enschede , postcode= 7522 NB , country= The Netherlands；organization= Department of Computer Science, Technical University of Munich , adressline= Boltzmannstr. 3 , city= Garching , postcode= 85748 , country= Germany；organization= Munich Center for Machine Learning , adressline= Boltzmannstr. 3 , city= Garching , postcode= 85748 , country= Germany；organization= Department of Surgery, Amsterdam UMC, Location University of Amsterdam , addressline= Meibergdreef 9 , city= Amsterdam , country= The Netherlands；organization= Department of Surgery, Amsterdam UMC, Location Vrije Universiteit Amsterdam , addressline= De Boelelaan 5 , city= Amsterdam , postcode= 1083 HJ , country= The Netherlands；organization= Amsterdam Cardiovascular Sciences, Atherosclerosis \& Aortic diseases , city= Amsterdam , country= The Netherlands
Interpretable Aneurysm Classification via 3D Concept Bottleneck Models: Integrating Morphological and Hemodynamic Clinical Features
Biomedical Sciences Program, School of Sciences, Zewail City of Science and Technology（泽瓦伊科学与技术城科学与技术学院生物医学科学项目）；Multimedia Interaction and Communication Lab, Arab Academy for Science and Technology（阿拉伯科学与技术学院多媒体交互与通信实验室）
