• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

颅内动脉瘤管理中放射组学和人工智能的系统评价

Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.

作者信息

Owens Monica-Rae, Tenhoeve Samuel A, Rawson Clayton, Azab Mohammed, Karsy Michael

机构信息

Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA.

College of Osteopathic Medicine, NOORDA College, Provo, Utah, USA.

出版信息

J Neuroimaging. 2025 Mar-Apr;35(2):e70037. doi: 10.1111/jon.70037.

DOI:10.1111/jon.70037
PMID:40095247
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11912304/
Abstract

Intracranial aneurysms, with an annual incidence of 2%-3%, reflect a rare disease associated with significant mortality and morbidity risks when ruptured. Early detection, risk stratification of high-risk subgroups, and prediction of patient outcomes are important to treatment. Radiomics is an emerging field using the quantification of medical imaging to identify parameters beyond traditional radiology interpretation that may offer diagnostic or prognostic significance. The general radiomic workflow involves image normalization and segmentation, feature extraction, feature selection or dimensional reduction, training of a predictive model, and validation of the said model. Artificial intelligence (AI) techniques have shown increasing interest in applications toward vascular pathologies, with some commercially successful software including AiDoc, RapidAI, and Viz.AI, as well as the more recent Viz Aneurysm. We performed a systematic review of 684 articles and identified 84 articles exploring the applications of radiomics and AI in aneurysm treatment. Most studies were published between 2018 and 2024, with over half of articles in 2022 and 2023. Studies included categories such as aneurysm diagnosis (25.0%), rupture risk prediction (50.0%), growth rate prediction (4.8%), hemodynamic assessment (2.4%), clinical outcome prediction (11.9%), and occlusion or stenosis assessment (6.0%). Studies utilized molecular data (2.4%), radiologic data alone (51.2%), clinical data alone (28.6%), and combined radiologic and clinical data (17.9%). These results demonstrate the current status of this emerging and exciting field. An increased pace of innovation in this space is likely with the expansion of clinical applications of radiomics and AI in multiple vascular pathologies.

摘要

颅内动脉瘤的年发病率为2%-3%,是一种罕见疾病,破裂时会带来显著的死亡和发病风险。早期检测、高危亚组的风险分层以及患者预后的预测对治疗至关重要。放射组学是一个新兴领域,利用医学成像的量化来识别超出传统放射学解释的参数,这些参数可能具有诊断或预后意义。一般的放射组学工作流程包括图像归一化和分割、特征提取、特征选择或降维、预测模型的训练以及该模型的验证。人工智能(AI)技术在血管疾病应用方面的关注度日益增加,一些商业上成功的软件包括AiDoc、RapidAI和Viz.AI,以及最近的Viz Aneurysm。我们对684篇文章进行了系统综述,确定了84篇探讨放射组学和AI在动脉瘤治疗中应用的文章。大多数研究发表于2018年至2024年之间,2022年和2023年发表的文章超过一半。研究类别包括动脉瘤诊断(25.0%)、破裂风险预测(50.0%)、生长率预测(4.8%)、血流动力学评估(2.4%)、临床结局预测(11.9%)以及闭塞或狭窄评估(6.0%)。研究使用了分子数据(2.4%)、仅放射学数据(51.2%)、仅临床数据(28.6%)以及放射学和临床数据相结合(17.9%)。这些结果展示了这个新兴且令人兴奋的领域的现状。随着放射组学和AI在多种血管疾病中的临床应用不断扩展,这个领域的创新步伐可能会加快。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/998c/11912304/7579fe7a6b4c/JON-35-0-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/998c/11912304/1cf4834bd37c/JON-35-0-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/998c/11912304/7579fe7a6b4c/JON-35-0-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/998c/11912304/1cf4834bd37c/JON-35-0-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/998c/11912304/7579fe7a6b4c/JON-35-0-g001.jpg

相似文献

1
Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.颅内动脉瘤管理中放射组学和人工智能的系统评价
J Neuroimaging. 2025 Mar-Apr;35(2):e70037. doi: 10.1111/jon.70037.
2
Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects.人工智能驱动的头颈部癌症放射组学:现状与未来展望。
Int J Med Inform. 2024 Aug;188:105464. doi: 10.1016/j.ijmedinf.2024.105464. Epub 2024 Apr 23.
3
AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study.基于人工智能的心脏CT衰减扫描检测肝脂肪变性及综合肝脏评估可增强全因死亡风险分层:一项多中心研究
medRxiv. 2025 Jun 11:2025.06.09.25329157. doi: 10.1101/2025.06.09.25329157.
4
Evaluating artificial intelligence models for rupture risk prediction in unruptured intracranial aneurysms: a focus on vessel geometry and hemodynamic insights.评估用于预测未破裂颅内动脉瘤破裂风险的人工智能模型:聚焦血管几何形状和血流动力学见解。
Neurosurg Rev. 2025 Jul 2;48(1):539. doi: 10.1007/s10143-025-03689-6.
5
Gaps in Artificial Intelligence Research for Rural Health in the United States: A Scoping Review.美国农村卫生人工智能研究的差距:一项范围综述
medRxiv. 2025 Jun 27:2025.06.26.25330361. doi: 10.1101/2025.06.26.25330361.
6
Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.在基层医疗机构或医院门诊环境中,如果患者出现以下症状和体征,可判断其是否患有 COVID-19。
Cochrane Database Syst Rev. 2022 May 20;5(5):CD013665. doi: 10.1002/14651858.CD013665.pub3.
7
Research status, hotspots and perspectives of artificial intelligence applied to pain management: a bibliometric and visual analysis.人工智能应用于疼痛管理的研究现状、热点与展望:一项文献计量学与可视化分析
Updates Surg. 2025 Jun 28. doi: 10.1007/s13304-025-02296-w.
8
[Volume and health outcomes: evidence from systematic reviews and from evaluation of Italian hospital data].[容量与健康结果:来自系统评价和意大利医院数据评估的证据]
Epidemiol Prev. 2013 Mar-Jun;37(2-3 Suppl 2):1-100.
9
A Systematic Review and Bibliometric Analysis of Applications of Artificial Intelligence and Machine Learning in Vascular Surgery.人工智能和机器学习在血管外科应用的系统评价与文献计量分析
Ann Vasc Surg. 2022 Sep;85:395-405. doi: 10.1016/j.avsg.2022.03.019. Epub 2022 Mar 24.
10
Cost-effectiveness of using prognostic information to select women with breast cancer for adjuvant systemic therapy.利用预后信息为乳腺癌患者选择辅助性全身治疗的成本效益
Health Technol Assess. 2006 Sep;10(34):iii-iv, ix-xi, 1-204. doi: 10.3310/hta10340.

本文引用的文献

1
Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms.基于影像组学的预测列线图用于评估颅内动脉瘤风险
Transl Stroke Res. 2025 Feb;16(1):79-87. doi: 10.1007/s12975-024-01268-3. Epub 2024 Jul 2.
2
Role of inflammatory mediators in intracranial aneurysms: A review.炎症介质在颅内动脉瘤中的作用:综述。
Clin Neurol Neurosurg. 2024 Jul;242:108329. doi: 10.1016/j.clineuro.2024.108329. Epub 2024 May 18.
3
Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model.
基于临床-影像组学联合模型预测小型颅内动脉瘤破裂状态
Heliyon. 2024 Apr 24;10(9):e30214. doi: 10.1016/j.heliyon.2024.e30214. eCollection 2024 May 15.
4
Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography.比较深度学习模型和传统逻辑回归模型在计算机断层扫描血管造影中识别不稳定囊状颅内动脉瘤的效果。
Quant Imaging Med Surg. 2024 Apr 3;14(4):2993-3005. doi: 10.21037/qims-23-1732. Epub 2024 Mar 28.
5
USING CONVOLUTIONAL NEURAL NETWORK-BASED SEGMENTATION FOR IMAGE-BASED COMPUTATIONAL FLUID DYNAMICS SIMULATIONS OF BRAIN ANEURYSMS: INITIAL EXPERIENCE IN AUTOMATED MODEL CREATION.基于卷积神经网络的分割技术在脑动脉瘤基于图像的计算流体动力学模拟中的应用:自动模型创建的初步经验
J Mech Med Biol. 2023 May;23(4). doi: 10.1142/s0219519423400559. Epub 2023 Jun 10.
6
Predicting postinterventional rupture of intracranial aneurysms using arteriography-derived radiomic features after pipeline embolization.利用管道栓塞术后血管造影衍生的影像组学特征预测颅内动脉瘤介入治疗后破裂
Front Neurol. 2024 Mar 7;15:1327127. doi: 10.3389/fneur.2024.1327127. eCollection 2024.
7
Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model.使用基于点云的深度学习模型评估颅内动脉瘤破裂风险。
Front Physiol. 2024 Feb 15;15:1293380. doi: 10.3389/fphys.2024.1293380. eCollection 2024.
8
Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.颅内动脉瘤介入栓塞术后神经功能的深度学习和机器学习预测模型
Front Neurol. 2024 Jan 24;15:1321923. doi: 10.3389/fneur.2024.1321923. eCollection 2024.
9
The Management of Intracranial Aneurysms: Current Trends and Future Directions.颅内动脉瘤的管理:当前趋势与未来方向
Neurol Int. 2024 Jan 3;16(1):74-94. doi: 10.3390/neurolint16010005.
10
Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms.基于机器学习的显微手术治疗未破裂颅内动脉瘤的结果预测。
Sci Rep. 2023 Dec 19;13(1):22641. doi: 10.1038/s41598-023-50012-8.