• 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

相似文献

1
Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview.人工智能在未破裂颅内动脉瘤中的作用:综述
Front Neurol. 2022 Feb 23;13:784326. doi: 10.3389/fneur.2022.784326. eCollection 2022.
2
Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives.人工智能在颅内动脉瘤管理中的应用:现状与未来展望。
AJNR Am J Neuroradiol. 2020 Mar;41(3):373-379. doi: 10.3174/ajnr.A6468. Epub 2020 Mar 12.
3
Protocol and Preliminary Results of the Establishment of Intracranial Aneurysm Database for Artificial Intelligence Application Based on CTA Images.基于CTA图像建立用于人工智能应用的颅内动脉瘤数据库的方案及初步结果
Front Neurol. 2022 Jul 19;13:932933. doi: 10.3389/fneur.2022.932933. eCollection 2022.
4
Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review.基于图像的人工智能在颅内动脉瘤中的分类、检测和分割性能:系统评价。
BMC Med Imaging. 2024 Jul 2;24(1):164. doi: 10.1186/s12880-024-01347-9.
5
Artificial Intelligence Applications in Intracranial Aneurysm: Achievements, Challenges and Opportunities.人工智能在颅内动脉瘤中的应用:成就、挑战与机遇。
Acad Radiol. 2022 Mar;29 Suppl 3:S201-S214. doi: 10.1016/j.acra.2021.06.013. Epub 2021 Aug 8.
6
The natural course of unruptured intracranial aneurysms in a Chinese cohort: protocol of a multi-center registration study in CIAP.未破裂颅内动脉瘤自然病程的中国队列研究:CIAP 多中心注册研究方案。
J Transl Med. 2019 Oct 22;17(1):349. doi: 10.1186/s12967-019-2092-z.
7
Artificial intelligence in medical imaging of the liver.人工智能在肝脏医学影像中的应用。
World J Gastroenterol. 2019 Feb 14;25(6):672-682. doi: 10.3748/wjg.v25.i6.672.
8
A Review of Artificial Intelligence in the Rupture Risk Assessment of Intracranial Aneurysms: Applications and Challenges.颅内动脉瘤破裂风险评估中的人工智能综述:应用与挑战
Brain Sci. 2023 Jul 11;13(7):1056. doi: 10.3390/brainsci13071056.
9
Quantitative proteomics analysis of differentially expressed proteins in ruptured and unruptured cerebral aneurysms by iTRAQ.iTRAQ 技术分析破裂与未破裂脑动脉瘤差异表达蛋白的定量蛋白质组学研究
J Proteomics. 2018 Jun 30;182:45-52. doi: 10.1016/j.jprot.2018.05.001. Epub 2018 May 3.
10
Artificial intelligence in thyroid ultrasound.甲状腺超声中的人工智能
Front Oncol. 2023 May 12;13:1060702. doi: 10.3389/fonc.2023.1060702. eCollection 2023.

引用本文的文献

1
Risk factors and predictive indicators of rupture in cerebral aneurysms.脑动脉瘤破裂的危险因素及预测指标
Front Physiol. 2024 Sep 5;15:1454016. doi: 10.3389/fphys.2024.1454016. eCollection 2024.
2
Current understanding of macrophages in intracranial aneurysm: relevant etiological manifestations, signaling modulation and therapeutic strategies.目前对颅内动脉瘤中巨噬细胞的认识:相关的病因表现、信号调节和治疗策略。
Front Immunol. 2024 Jan 8;14:1320098. doi: 10.3389/fimmu.2023.1320098. eCollection 2023.
3
Malpractice Litigation Related to Diagnosis and Treatment of Intracranial Aneurysms.与颅内动脉瘤的诊断和治疗相关的医疗事故诉讼。
AJNR Am J Neuroradiol. 2023 Apr;44(4):460-466. doi: 10.3174/ajnr.A7828. Epub 2023 Mar 30.
4
Prediction and analysis of periprocedural complications associated with endovascular treatment for unruptured intracranial aneurysms using machine learning.使用机器学习对未破裂颅内动脉瘤血管内治疗相关围手术期并发症进行预测与分析。
Front Neurol. 2022 Oct 12;13:1027557. doi: 10.3389/fneur.2022.1027557. eCollection 2022.

本文引用的文献

1
The Aneurysm Occlusion Assistant, an AI platform for real time surgical guidance of intracranial aneurysms.动脉瘤闭塞助手,一个用于颅内动脉瘤实时手术指导的人工智能平台。
Proc SPIE Int Soc Opt Eng. 2021 Feb;11601. doi: 10.1117/12.2581003. Epub 2021 Feb 15.
2
AI system outperforms humans in designing floorplans for microchips.人工智能系统在设计微芯片布局图方面表现优于人类。
Nature. 2021 Jun;594(7862):183-185. doi: 10.1038/d41586-021-01515-9.
3
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.一种可应用于临床的基于深度学习的 CT 血管造影图像颅内动脉瘤检测模型。
Nat Commun. 2020 Nov 30;11(1):6090. doi: 10.1038/s41467-020-19527-w.
4
Predictive score for complete occlusion of intracranial aneurysms treated by flow-diverter stents using machine learning.基于机器学习的血流导向装置治疗颅内动脉瘤完全闭塞的预测评分。
J Neurointerv Surg. 2021 Apr;13(4):341-346. doi: 10.1136/neurintsurg-2020-016748. Epub 2020 Nov 20.
5
Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.深度学习在 CT 血管造影中检测脑动脉瘤的应用
Radiology. 2021 Jan;298(1):155-163. doi: 10.1148/radiol.2020192154. Epub 2020 Nov 3.
6
Incidental cerebral aneurysms detected by a computer-assisted detection system based on artificial intelligence: A case series.基于人工智能的计算机辅助检测系统检测出的偶然脑动脉瘤:病例系列
Medicine (Baltimore). 2020 Oct 23;99(43):e21518. doi: 10.1097/MD.0000000000021518.
7
Management of incidental unruptured intracranial aneurysms.偶然发现的未破裂颅内动脉瘤的处理。
Pract Neurol. 2020 Oct;20(5):347-355. doi: 10.1136/practneurol-2020-002521. Epub 2020 Sep 6.
8
Stability Assessment of Intracranial Aneurysms Using Machine Learning Based on Clinical and Morphological Features.基于临床和形态学特征的机器学习在颅内动脉瘤稳定性评估中的应用。
Transl Stroke Res. 2020 Dec;11(6):1287-1295. doi: 10.1007/s12975-020-00811-2. Epub 2020 May 19.
9
Development and validation of machine learning prediction model based on computed tomography angiography-derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study.基于 CT 血管造影血流动力学的颅内动脉瘤破裂状态的机器学习预测模型的建立与验证:一项中国多中心研究。
Eur Radiol. 2020 Sep;30(9):5170-5182. doi: 10.1007/s00330-020-06886-7. Epub 2020 Apr 29.
10
Prediction of Intracranial Aneurysm Risk using Machine Learning.基于机器学习的颅内动脉瘤风险预测。
Sci Rep. 2020 Apr 24;10(1):6921. doi: 10.1038/s41598-020-63906-8.

人工智能在未破裂颅内动脉瘤中的作用:综述

Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview.

作者信息

Marasini Anurag, Shrestha Alisha, Phuyal Subash, Zaidat Osama O, Kalia Junaid Siddiq

机构信息

AINeuroCare Academy, Dallas, TX, United States.

Travel and Mountain Medicine Center, Kathmandu, Nepal.

出版信息

Front Neurol. 2022 Feb 23;13:784326. doi: 10.3389/fneur.2022.784326. eCollection 2022.

DOI:10.3389/fneur.2022.784326
PMID:35280303
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8904392/
Abstract

Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. Artificial intelligence (AI) has received worldwide attention for its impressive performance in image-based tasks. It can serve as an adjunct to physicians in clinical settings, improving diagnostic accuracy while reducing physicians' workload. AI can perform tasks such as pattern recognition, object identification, and problem resolution with human-like intelligence. Based on the data collected for training, AI can assist in decisions in a semi-autonomous manner. Similarly, AI can identify a likely diagnosis and also, select a suitable treatment based on health records or imaging data without any explicit programming (instruction set). Aneurysm rupture prediction is the holy grail of prediction modeling. AI can significantly improve rupture prediction, saving lives and limbs in the process. Nowadays, deep learning (DL) has shown significant potential in accurately detecting lesions on medical imaging and has reached, or perhaps surpassed, an expert-level of diagnosis. This is the first step to accurately diagnose UIAs with increased computational radiomicis. This will not only allow diagnosis but also suggest a treatment course. In the future, we will see an increasing role of AI in both the diagnosis and management of IAs.

摘要

颅内动脉瘤(IAs)是一个重大的公共卫生问题。在无合并症且平均年龄为50岁的人群中,其患病率高达3.2%。因此,需要一种有效的方法来识别颅内动脉瘤高风险患者,以提供适当的放射学筛查指南并有效分配医疗资源。人工智能(AI)因其在基于图像的任务中令人印象深刻的表现而受到全球关注。它可以在临床环境中作为医生的辅助工具,提高诊断准确性,同时减轻医生的工作量。人工智能可以执行模式识别、目标识别和问题解决等任务,具备类似人类的智能。基于收集用于训练的数据,人工智能可以以半自主的方式协助做出决策。同样,人工智能可以在没有任何明确编程(指令集)的情况下,根据健康记录或成像数据识别可能的诊断,并选择合适的治疗方法。动脉瘤破裂预测是预测建模的圣杯。人工智能可以显著改善破裂预测,在此过程中挽救生命和肢体。如今,深度学习(DL)在医学影像上准确检测病变方面已显示出巨大潜力,并且已经达到或可能超过了专家级诊断水平。这是通过增加计算放射组学来准确诊断未破裂颅内动脉瘤的第一步。这不仅可以实现诊断,还可以建议治疗方案。未来,我们将看到人工智能在颅内动脉瘤的诊断和管理中发挥越来越重要的作用。