[1]凯赛尔江·卡地尔 艾克力亚尔·艾尼瓦尔 秦练 热娜·热合木丁 马翔.深度学习在冠心病影像学诊断的研究进展[J].心血管病学进展,2022,(4):335-340.[doi:10.16806/j.cnki.issn.1004-3934.2022.04.012]
　Kaisaierjiang Kadier,Aikeliyaer Ainiwae,rQIN Lian,et al.Deep Learning in Imaging Diagnosis of Coronary Heart Disease[J].Advances in Cardiovascular Diseases,2022,(4):335-340.[doi:10.16806/j.cnki.issn.1004-3934.2022.04.012]

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深度学习在冠心病影像学诊断的研究进展(/HTML)

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参考文献/References:

[1 ] 胡盛寿 , 高 润霖 , 刘力生 , 等 .?span style="font-family: 宋体;">《中国心血管病报告2018》概要[J].?span style="font-family: 宋体;">中国循环杂志,2019,34(3):209-220.

[2 ] 凯赛尔江 ·卡地尔 , 艾克力亚尔·艾尼瓦尔 , 马翔 .?span style="font-family: 宋体;">人工智能在主动脉夹层影像学诊断与风险预测中的应用[J].?span style="font-family: 宋体;">中华心血管病杂志,2021,49(11):1152-1156.

[3 ]Premaladha J , Ravichandran KS. Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms[J].?span style="font-family: "Times New Roman";">J Med Syst,2016,40(4):96.

[4]Botvinick M,Wang JX,Dabney W,et al. Deep reinforcement learning and its neuroscientific implications[J]. Neuron,2020,107(4):603-616.

[5 ]Wen B , Zeng WF , Liao Y , et al. Deep learning in proteomics[J]. Proteomics , 2020 , 20(21-22) : e 1900335.

[6 ]Sodickson A , Baeyens PF , Andriole KP , et al. Recurrent CT , cumulative radiation exposure , and associated radiation-induced cancer risks from CT of adults[J]. Radiology , 2009 , 251(1) : 175-184.

[7 ]Benz DC , Fuchs TA , Gr?i C , et al. Head-to-head comparison of adaptive statistical and model-based iterative reconstruction algorithms for submillisievert coronary CT angiography[J].?/span>Eur Heart J Cardiovasc Imaging,2018,19(2):193-198.

[8 ]Liu P , Wang M , Wang Y , et al. Impact of deep learning-based optimization algorithm on image quality of low-dose coronary CT angiography with noise reduction : a prospective study[J].?/span>Acad Radiol,2020,27(9):1241-1248.

[9 ]Benz DC , Benetos G , Rampidis G , et al. Validation of deep-learning image reconstruction for coronary computed tomography angiography : impact on noise , image quality and diagnostic accuracy[J].?/span>J Cardiovasc Comput Tomogr,2020,14(5):444-451.

[10 ]Hong JH , Park EA , Lee W , et al. Incremental image noise reduction in coronary CT angiography using a deep learning-based technique with iterative reconstruction[J].?span style="font-family: "Times New Roman";">Korean J Radiol,2020,21(10):1165-1177.

[11 ]Lossau T , Nickisch H , Wissel T , et al. Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks[J].?/span>Med Image Anal,2019,52:68-79.

[12 ]Jung S , Lee S , Jeon B , et al. Deep learning cross-phase style transfer for motion artifact correction in coronary computed tomography angiography[J]. IEEE Access , 2020 , 8 : 81849-81863.

[13 ]Xiao C , Li Y , Jiang Y. Heart coronary artery segmentation and disease risk warning based on a deep learning algorithm[J]. IEEE Access , 2020 , 8 : 140108-140121.

[14 ]Jun Guo B , He X , Lei Y , et al. Automated left ventricular myocardium segmentation using 3D deeply supervised attention U-net for coronary computed tomography angiography; CT myocardium segmentation[J].?/span>Med Phys,2020,47(4):1775-1785.

[15 ]?span style="font-family: "Times New Roman";">He X,Guo B J,Lei Y,et al. Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography[J].?/span>Phys Med Biol,2020,65(9):095012.

[16 ]?span style="font-family: "Times New Roman";">Commandeur F,Goeller M,Betancur J,et al. Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT[J].?/span>IEEE Trans Med Imaging,2018,37(8):1835-1846.

[17 ] 李宸 , 徐少华 , 陆丽洁 , 等 .?span style="font-family: 宋体;">基于冠状动脉CT血管成像的无创血流储备分数的研究进展[J].?span style="font-family: 宋体;">心血管病学进展,2021,42(5):445-448.

[18 ]Wang ZQ , Zhou YJ , Zhao YX , et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography[J].?/span>J Geriatr Cardiol,2019,16(1):42-48.

[19 ]Kumamaru KK , Fujimoto S , Otsuka Y , et al. Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography[J].?/span>Eur Heart J Cardiovasc Imaging,2020,21(4):437-445.

[20 ]Zreik M , van Hamersvelt RW , Khalili N , et al. Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography[J].?span style="font-family: "Times New Roman";">IEEE Trans Med Imaging,2020,39(5):1545-1557.

[21 ]Hecht HS.?span style="font-family: "Times New Roman";">Coronary artery calcium scanning:past,present,and future[J].?/span>JACC Cardiovasc Imaging,2015,8(5):579-596.

[22 ]van den Oever LB , Cornelissen L , Vonder M , et al. Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium[J]. Eur J Radiol , 2020 , 129 : 109114.

[23 ]Lessmann N , van Ginneken B , Zreik M , et al. Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions[J].?/span>IEEE Trans Med Imaging,2018,37(2):615-625.

[24 ]Zhang N , Yang G , Zhang W , et al. Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan : total and vessel-specific quantifications[J].?/span>Eur J Radiol,2021,134:109420.

[25 ]van Velzen SGM , Lessmann N , Velthuis BK , et al. Deep learning for automatic calcium scoring in CT : validation using multiple cardiac CT and chest CT protocols[J]. Radiology , 2020 , 295(1) : 66-79.

[26 ] Tan LK , McLaughlin RA , Lim E , et al. Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression[J]. J Magn Reson Imaging , 2018 , 48(1) : 140-152.

[27 ] Hosseini Hosseini?span style="font-family: "Times New Roman";">SA,Moeller S,Weing?tner S,et al.?/span>Accelerated coronary MRI using 3D SPIRiT-RAKI with sparsity regularization[J].?span style="font-family: "Times New Roman";">Proc IEEE Int Symp Biomed Imaging,2019,2019:1692-1695.

[28 ]Scannell CM , Veta M , Villa ADM , et al.?span style="font-family: "Times New Roman";">Deep-learning-based preprocessing for quantitative myocardial perfusion MRI[J].?/span>J Magn Reson Imaging,2020,51(6):1689-1696.

[29 ]Zhang N , Yang G , Gao Z , et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI[J]. Radiology , 2019 , 291(3) : 606-617.

[30 ]Shibutani T , Nakajima K , Wakabayashi H , et al. Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT[J].?/span>Ann Nucl Med,2019,33(2):86-92.

[31 ] Kaplan Berkaya S , Ak Sivrikoz I , Gunal S . Classification models for SPECT myocardial perfusion imaging[J].?/span>Comput Biol Med,2020,123:103893.

[32 ]Betancur J , Commandeur F , Motlagh M , et al.?/span>Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study[J].?/span>JACC Cardiovasc Imaging,2018,11(11):1654-1663.

[33 ]Betancur J , Hu LH , Commandeur F , et al.?/span>Deep learning analysis of upright-supine high-efficiency SPECT myocardial perfusion imaging for prediction of obstructive coronary artery disease: a multicenter study[J].?/span>J Nucl Med,2019,60(5):664-670.

[34 ] Otaki Y , Singh A , Kavanagh P , et al. Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease[J].?span style="font-family: "Times New Roman";">JACC Cardiovasc Imaging,2021,S1936-878X(21)00438-1.

[35 ] Liu H , Wu J , Miller EJ , et al. Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning[J]. Eur J Nucl Med Mol Imaging , 2021 , 48(9):2793-2800.

[36 ]Gessert N , Lutz M , Heyder M , et al.?/span>Automatic plaque detection in IVOCT pullbacks using convolutional neural networks[J].?/span>IEEE Trans Med Imaging,2019,38(2):426-434.

[37 ]Gharaibeh Y , Prabhu DS , Kolluru C , et al. Coronary calcification segmentation in intravascular OCT images using deep learning : application to calcification scoring[J].?/span>J Med Imaging (Bellingham),2019,6(4):045002.

[38]Liu X,Du J,Yang J,et al. Coronary?/span>artery?/span>fibrous?/span>plaque?/span>detection?/span>based on?/span>multi-scale?/span>convolutional?/span>neural?/span>networks[J].?/span>J Sign Process Syst,2020,92(3):325-333.

[39 ]Kusunose K , Abe T , Haga A , et al.?/span>A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images[J].?span style="font-family: "Times New Roman";">JACC Cardiovasc Imaging , 2020 , 13(2 Pt 1):374-381 .

[40 ]?span style="font-family: "Times New Roman";">Ouyang D,He B,Ghorbani A,et al. Video-based AI for beat-to-beat assessment of cardiac function[J]. Nature,2020,580(7802):252-256.

[41]Huang MS,Wang CS,Chiang JH,et al.?span style="font-family: "Times New Roman";">Automated recognition of regional wall motion abnormalities through deep neural network interpretation of transthoracic echocardiography[J].?span style="font-family: "Times New Roman";">Circulation,2020,142(16):1510-1520.

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