人工智能在心脏影像诊断中的研究进展(4) - 心脏杂志杂志社投稿_期刊论文发表|版面费|电话|编辑部- 心脏杂志

一、本刊要求作者有严谨的学风和朴实的文风，提倡互相尊重和自由讨论。凡采用他人学说，必须加注说明。二、不要超过10000字为宜，精粹的短篇，尤为欢迎。三、请作者将稿件（用WORD格式）发送到下面给出的征文信箱中。四、凡来稿请作者自留底稿，恕不退稿。五、为规范排版，请作者在上传修改稿时严格按以下要求： 1．论文要求有题名、摘要、关键词、作者姓名、作者工作单位（名称，省市邮编）等内容一份。 2．基金项目和作者简介按下列格式：基金项目：项目名称（编号）作者简介：姓名（出生年－），性别，民族（汉族可省略），籍贯，职称，学位，研究方向。 3．文章一般有引言部分和正文部分，正文部分用阿拉伯数字分级编号法，一般用两级。插图下方应注明图序和图名。表格应采用三线表，表格上方应注明表序和表名。 4．参考文献列出的一般应限于作者直接阅读过的、最主要的、发表在正式出版物上的文献。其他相关注释可用脚注在当页标注。参考文献的著录应执行国家标准GB7714-87的规定，采用顺序编码制。

人工智能在心脏影像诊断中的研究进展(4)

作者:

关键词:

摘要：

[12] Genovese D, Rashedi N, Weinert L, et learning-based three-dimensional echocardiographic quantification of right ventricular size and function:validation against cardiac magnetic resonance[J].J Am Soc Echocardiogr,2019,32:969-977.

[13] Ghosh S, Chattopadhyay BP, Roy RM, et of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks[J].Artif Intell Med,2019,96:45-58.

[14] Attia ZI,Kapa S,Lopez-Jimenez F,et for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram[J].Nat Med,2019,25:70-74.

[15] Chen H, Zhang Y, Zhang W, et al. Low-dose CT via convolutional neural network[J].Biomed Opt Express,2017,8:679-694.

[16] Yang Q,Yan P,Zhang Y,et al.Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss[J].IEEE Trans Med Imaging,2018,37:1348-1357.

[17] Tatsugami F,Higaki T,Nakamura Y,et al.Deep learning-based image restoration algorithm for coronary CT angiography[J].Eur Radiol,2019,29:5322-5329.

[18] Takx RA,De JPA,Tim L,et coronary artery calcification scoring in non-gated chest CT: agreement and reliability[J].PLoS One,2014,9:e.

[19] Kang D, Dey D, Slomka PJ, et al. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography [J].J Med Imaging(Bellingham),2015,2:0.

[20] Johnson KM,Johnson HE,Zhao Y,et al.基于机器学习的冠状动脉CT 血管成像上的冠状动脉病变特征评分[J].国际医学放射学杂志,2019,42:615-616.

[21] 乔红艳, 张龙江. 基于影像的计算流体力学在冠状动脉疾病中的研究进展[J]. 国际医学放射学杂志,2019,42:678-683.

[22] Tesche C,De Cecco CN,Albrecht MH,et CT angiography-derived fractional flow reserve[J].Radiology,2017,285:17-33.

[23] Dey D, Gaur S, Ovrehus KA, et prediction of lesionspecific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study[J]. Eur Radiol, 2018, 28:2655-2664.

[24] Commandeur F, Goeller M, Betancur J, et al. Deep learning for quantification of epicardial and thoracic adipose tissue from noncontrast CT[J].IEEE Trans Med Imaging, 2018,37:1835-1846.

[25] Zreik M, Lessmann N, van Hamersvelt RW, et al. Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis[J].Med Image Anal,2018,44:72-85.

[26] Dey D, Slomka PJ, Leeson P, et intelligence in cardiovascular imaging:JACC state-of-the-art review[J].J Am Coll Cardiol,2019,73:1317-1335.

[27] Itu L,Rapaka S,Passerini T,et al.A machine-learning approach for computation of fractional flow reserve from coronary computed tomography[J].J App Physiol,2016,121:42-52.

[28] Miotto R,Li L,Kidd BA,et al.Deep patient:an unsupervised representation to predict the future of patients from the electronic health records[J].Sci Rep,2016,6:.

[29] Motwani M, Dey D, Berman DS, et learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis[J].Eur Heart J,2017,38:500-507.

[30] Karim R,Bhagirath P,Claus P,et of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images[J].Med Image Anal,2016,30:95-107.

[31] Baessler B, Mannil M, Oebel S, et al. Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images[J].Radiology,2018,286:103-112.

[32] Bernard O, Lalande A, Zotti C, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis:is the problem solved[J].IEEE Trans Med Imaging,2018,37:2514-2525.

[33] Dawes TJW,Marvao AD,Shi W,et learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension:a cardiac MR imaging study [J].Radiology,2017,283:381-390.

[34] Slomka PJ, Dey D, Sitek A, et al. Cardiac imaging: working towards fully-automated machine analysis &interpretation [J]. Expert Rev Med Devices,2017,14:197-212.

[35] Driessen RS, Raijmakers PG, Danad I, et SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease[J].Eur J Nucl Med Mol Imaging,2018,45:1091-1100.

[36] Nakajima K,Kudo T,Nakata T,et accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study[J]. Eur J Nucl Med Mol Imaging,2017,44:2280-2289.

[37] Nakajima K, Okuda K, Watanabe S, et neural network retrained to detect myocardial ischemia using a Japanese multicenter database[J].Ann Nucl Med,2018,32:303-310.

[38] Frizzell JD,Liang L,Schulte PJ,et of 30-day all-cause readmissions in patients hospitalized for heart failure:comparison of machine learning and other statistical approaches[J].JAMA Cardiol,2017,2:204-209.

[39] Arsanjani R, Xu Y, Dey D, et al. Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm [J]. J Nucl Med, 2013, 54: 549-555.

文章来源：《心脏杂志》网址: http://www.xzzzzzs.cn/qikandaodu/2021/0412/741.html