• 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

英国生物银行人群队列中左心室质量和容积的全自动MRI分析:初步结果评估

Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results.

作者信息

Suinesiaputra Avan, Sanghvi Mihir M, Aung Nay, Paiva Jose Miguel, Zemrak Filip, Fung Kenneth, Lukaschuk Elena, Lee Aaron M, Carapella Valentina, Kim Young Jin, Francis Jane, Piechnik Stefan K, Neubauer Stefan, Greiser Andreas, Jolly Marie-Pierre, Hayes Carmel, Young Alistair A, Petersen Steffen E

机构信息

Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland, 1142, New Zealand.

William Harvey Research Institute, NIHR Cardiovascular Biomedical Research Centre at Barts, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.

出版信息

Int J Cardiovasc Imaging. 2018 Feb;34(2):281-291. doi: 10.1007/s10554-017-1225-9. Epub 2017 Aug 23.

DOI:10.1007/s10554-017-1225-9
PMID:28836039
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5809564/
Abstract

UK Biobank, a large cohort study, plans to acquire 100,000 cardiac MRI studies by 2020. Although fully-automated left ventricular (LV) analysis was performed in the original acquisition, this was not designed for unsupervised incorporation into epidemiological studies. We sought to evaluate automated LV mass and volume (Siemens syngo InlineVF versions D13A and E11C), against manual analysis in a substantial sub-cohort of UK Biobank participants. Eight readers from two centers, trained to give consistent results, manually analyzed 4874 UK Biobank cases for LV end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF) and LV mass (LVM). Agreement between manual and InlineVF automated analyses were evaluated using Bland-Altman analysis and the intra-class correlation coefficient (ICC). Tenfold cross-validation was used to establish a linear regression calibration between manual and InlineVF results. InlineVF D13A returned results in 4423 cases, whereas InlineVF E11C returned results in 4775 cases and also reported LVM. Rapid visual assessment of the E11C results found 178 cases (3.7%) with grossly misplaced contours or landmarks. In the remaining 4597 cases, LV function showed good agreement: ESV -6.4 ± 9.0 ml, 0.853 (mean ± SD of the differences, ICC) EDV -3.0 ± 11.6 ml, 0.937; SV 3.4 ± 9.8 ml, 0.855; and EF 3.5 ± 5.1%, 0.586. Although LV mass was consistently overestimated (29.9 ± 17.0 g, 0.534) due to larger epicardial contours on all slices, linear regression could be used to correct the bias and improve accuracy. Automated InlineVF results can be used for case-control studies in UK Biobank, provided visual quality control and linear bias correction are performed. Improvements between InlineVF D13A and InlineVF E11C show the field is rapidly advancing, with further improvements expected in the near future.

摘要

英国生物银行是一项大型队列研究,计划到2020年获取10万份心脏磁共振成像研究资料。尽管在最初采集时进行了全自动左心室(LV)分析,但这并非为无监督地纳入流行病学研究而设计。我们试图在英国生物银行参与者的一个相当大的子队列中,将自动LV质量和容积(西门子syngo InlineVF版本D13A和E11C)与手动分析进行对比评估。来自两个中心的8名读者经过培训以给出一致的结果,他们手动分析了4874例英国生物银行病例的左心室舒张末期容积(EDV)、收缩末期容积(ESV)、每搏输出量(SV)、射血分数(EF)和左心室质量(LVM)。使用Bland-Altman分析和组内相关系数(ICC)评估手动分析和InlineVF自动分析之间的一致性。采用十折交叉验证来建立手动分析和InlineVF结果之间的线性回归校准。InlineVF D13A在4423例病例中返回了结果,而InlineVF E11C在4775例病例中返回了结果并且还报告了LVM。对E11C结果的快速视觉评估发现178例(3.7%)病例的轮廓或地标严重错位。在其余4597例病例中,左心室功能显示出良好的一致性:ESV -6.4±9.0毫升,差异的均值±标准差为0.853(ICC);EDV -3.0±11.6毫升,0.937;SV 3.4±9.8毫升,0.855;EF 3.5±5.1%,0.586。尽管由于所有切片上的心外膜轮廓较大,LV质量一直被高估(29.9±17.0克,0.534),但可以使用线性回归来校正偏差并提高准确性。只要进行视觉质量控制和线性偏差校正,自动InlineVF结果可用于英国生物银行的病例对照研究。InlineVF D13A和InlineVF E11C之间的改进表明该领域正在迅速发展,预计在不久的将来会有进一步的改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/50a15a33c7c9/10554_2017_1225_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/8777ed06ae7f/10554_2017_1225_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/a52c1ab328a3/10554_2017_1225_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/5266b5eed6ec/10554_2017_1225_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/50a15a33c7c9/10554_2017_1225_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/8777ed06ae7f/10554_2017_1225_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/a52c1ab328a3/10554_2017_1225_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/5266b5eed6ec/10554_2017_1225_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b49/5809564/50a15a33c7c9/10554_2017_1225_Fig4_HTML.jpg

相似文献

1
Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results.英国生物银行人群队列中左心室质量和容积的全自动MRI分析:初步结果评估
Int J Cardiovasc Imaging. 2018 Feb;34(2):281-291. doi: 10.1007/s10554-017-1225-9. Epub 2017 Aug 23.
2
Fully automated quantification of biventricular volumes and function in cardiovascular magnetic resonance: applicability to clinical routine settings.全心室容积和功能的全自动心血管磁共振定量分析:在临床常规环境中的适用性。
J Cardiovasc Magn Reson. 2019 Apr 25;21(1):24. doi: 10.1186/s12968-019-0532-9.
3
Compressed sensing real-time cine cardiovascular magnetic resonance: accurate assessment of left ventricular function in a single-breath-hold.压缩感知实时电影心血管磁共振成像:单次屏气下对左心室功能的准确评估
J Cardiovasc Magn Reson. 2016 Aug 24;18(1):50. doi: 10.1186/s12968-016-0271-0.
4
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.基于全卷积网络的自动化心血管磁共振图像分析。
J Cardiovasc Magn Reson. 2018 Sep 14;20(1):65. doi: 10.1186/s12968-018-0471-x.
5
Feasibility, accuracy, and reproducibility of real-time full-volume 3D transthoracic echocardiography to measure LV volumes and systolic function: a fully automated endocardial contouring algorithm in sinus rhythm and atrial fibrillation.实时全容积 3D 经胸超声心动图测量左心室容积和收缩功能的可行性、准确性和可重复性:窦性心律和心房颤动中全自动心内膜轮廓算法。
JACC Cardiovasc Imaging. 2012 Mar;5(3):239-51. doi: 10.1016/j.jcmg.2011.12.012.
6
Quantification in cardiovascular magnetic resonance: agreement of software from three different vendors on assessment of left ventricular function, 2D flow and parametric mapping.心血管磁共振中的定量分析:三种不同供应商的软件在评估左心室功能、二维流和参数映射方面的一致性。
J Cardiovasc Magn Reson. 2019 Feb 21;21(1):12. doi: 10.1186/s12968-019-0522-y.
7
Accuracy and Time-Efficiency of an Automated Software Tool to Assess Left Ventricular Parameters in Cardiac Magnetic Resonance Imaging.评估心脏磁共振左心室参数的自动化软件工具的准确性和效率。
J Thorac Imaging. 2020 Jan;35(1):64-70. doi: 10.1097/RTI.0000000000000459.
8
Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging.年龄和性别对心脏磁共振成像中左心室功能、容积和轮廓的全自动深度学习评估的影响。
Int J Cardiovasc Imaging. 2021 Dec;37(12):3539-3547. doi: 10.1007/s10554-021-02326-9. Epub 2021 Jun 29.
9
Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.心脏磁共振成像中左心室容积和功能的全自动定量分析:基于深度学习算法的临床评估
Int J Cardiovasc Imaging. 2020 Nov;36(11):2239-2247. doi: 10.1007/s10554-020-01935-0. Epub 2020 Jul 16.
10
Unsupervised fully automated inline analysis of global left ventricular function in CINE MR imaging.电影磁共振成像中左心室整体功能的无监督全自动在线分析。
Invest Radiol. 2009 Aug;44(8):463-8. doi: 10.1097/RLI.0b013e3181aaf429.

引用本文的文献

1
Automated Deep Learning Phenotyping of Tricuspid Regurgitation in Echocardiography.超声心动图中三尖瓣反流的自动化深度学习表型分析
JAMA Cardiol. 2025 Apr 16. doi: 10.1001/jamacardio.2025.0498.
2
Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data.基于深度学习的 T1 映射 MRI 数据心肌分割全自动模型的开发与性能评估。
Sci Rep. 2024 Aug 14;14(1):18895. doi: 10.1038/s41598-024-69529-7.
3
A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data.

本文引用的文献

1
Precision Imaging: more descriptive, predictive and integrative imaging.精准成像:更具描述性、预测性和整合性的成像技术。
Med Image Anal. 2016 Oct;33:27-32. doi: 10.1016/j.media.2016.06.024. Epub 2016 Jun 23.
2
Cardiac image modelling: Breadth and depth in heart disease.心脏影像建模:心脏病研究的广度与深度
Med Image Anal. 2016 Oct;33:38-43. doi: 10.1016/j.media.2016.06.027. Epub 2016 Jun 17.
3
Learning clinically useful information from images: Past, present and future.从图像中学习临床有用信息:过去、现在和未来。
一种利用英国生物银行迪克森MRI数据对骨髓脂肪进行大规模分析的新型深度学习方法。
Comput Struct Biotechnol J. 2023 Dec 27;24:89-104. doi: 10.1016/j.csbj.2023.12.029. eCollection 2024 Dec.
4
A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.一种基于多模态视频的人工智能生物标志物用于主动脉瓣狭窄的发生和进展
medRxiv. 2024 Feb 29:2023.09.28.23296234. doi: 10.1101/2023.09.28.23296234.
5
Genetically predicted androgenic profiles and adverse cardiac markers: a sex-specific Mendelian randomization study.遗传预测的雄激素特征与不良心脏标志物:一项性别特异性孟德尔随机化研究。
ESC Heart Fail. 2023 Dec;10(6):3525-3537. doi: 10.1002/ehf2.14527. Epub 2023 Sep 22.
6
Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification.心血管成像中的人工智能:增强图像分析与风险分层
BJR Open. 2023 May 17;5(1):20220021. doi: 10.1259/bjro.20220021. eCollection 2023.
7
Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping.使用时空映射校正多模态图像衍生的心脏几何结构中的偏差。
Sci Rep. 2023 May 19;13(1):8118. doi: 10.1038/s41598-023-33968-5.
8
Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass.深度学习衍生的心脏磁共振左心室质量的临床和遗传相关性。
Nat Commun. 2023 Mar 21;14(1):1558. doi: 10.1038/s41467-023-37173-w.
9
Frequency, Penetrance, and Variable Expressivity of Dilated Cardiomyopathy-Associated Putative Pathogenic Gene Variants in UK Biobank Participants.英国生物库参与者中扩张型心肌病相关疑似致病基因突变的频率、外显率和表现度。
Circulation. 2022 Jul 12;146(2):110-124. doi: 10.1161/CIRCULATIONAHA.121.058143. Epub 2022 Jun 16.
10
Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements.人工智能衍生右心房心血管磁共振测量的培训和临床测试。
J Cardiovasc Magn Reson. 2022 Apr 7;24(1):25. doi: 10.1186/s12968-022-00855-3.
Med Image Anal. 2016 Oct;33:13-18. doi: 10.1016/j.media.2016.06.009. Epub 2016 Jun 15.
4
Relationship between body composition and left ventricular geometry using three dimensional cardiovascular magnetic resonance.使用三维心血管磁共振成像研究身体成分与左心室几何结构之间的关系。
J Cardiovasc Magn Reson. 2016 May 31;18(1):32. doi: 10.1186/s12968-016-0251-4.
5
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.一种联合深度学习和可变形模型的方法,用于心脏 MRI 中左心室的全自动分割。
Med Image Anal. 2016 May;30:108-119. doi: 10.1016/j.media.2016.01.005. Epub 2016 Feb 6.
6
UK Biobank's cardiovascular magnetic resonance protocol.英国生物银行的心血管磁共振检查方案。
J Cardiovasc Magn Reson. 2016 Feb 1;18:8. doi: 10.1186/s12968-016-0227-4.
7
A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging.使用心脏磁共振成像进行心脏腔室分割以进行结构和功能分析的综述。
MAGMA. 2016 Apr;29(2):155-95. doi: 10.1007/s10334-015-0521-4. Epub 2016 Jan 25.
8
Automated Assessment of Left Ventricular Function and Mass Using Heart Deformation Analysis: Initial Experience in 160 Older Adults.使用心脏变形分析自动评估左心室功能和质量:160名老年人的初步经验。
Acad Radiol. 2016 Mar;23(3):321-5. doi: 10.1016/j.acra.2015.10.020. Epub 2015 Dec 31.
9
Precursors of Hypertensive Heart Phenotype Develop in Healthy Adults: A High-Resolution 3D MRI Study.健康成年人中出现高血压性心脏表型的前驱特征:一项高分辨率3D MRI研究
JACC Cardiovasc Imaging. 2015 Nov;8(11):1260-9. doi: 10.1016/j.jcmg.2015.08.007. Epub 2015 Oct 14.
10
Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours.通过心血管磁共振对左心室功能和质量进行量化:多中心变异性与共识轮廓
J Cardiovasc Magn Reson. 2015 Jul 28;17(1):63. doi: 10.1186/s12968-015-0170-9.