Page 149 - 南京医科大学自然版
P. 149

第44卷第8期            唐蛰雨,李绍钦,贾中芝. 基于CT图像的深度学习在主动脉夹层中的应用进展[J].
                  2024年8月                     南京医科大学学报(自然科学版),2024,44(8):1174-1178                      ·1177 ·


               [参考文献]                                                  learning algorithm to detect fibrosing interstitial lung dis⁃
                                                                       ease on chest radiographs[J]. Eur Respir J,2023,61(2):
               [1] ISSELBACHER E M,PREVENTZA O,HAMILTON
                                                                       2102269
                    BLACK J,3RD,et al. 2022 ACC/AHA Guideline for the
                                                                 [14]CAO K,XIA Y,YAO J,et al. Large⁃scale pancreatic can⁃
                    diagnosis and management of aortic disease:a report of the
                                                                       cer detection via non⁃contrast CT and deep learning[J].
                    AmericanheartAssociation/AmericanCollegeofCardiology
                                                                       Nat Med,2023,29(12):3033-3043
                    Joint Committee on clinical practice guidelines[J]. Circu⁃
                                                                 [15] SHKOLYAR E,JIA X,CHANG T C,et al. Augmented
                    lation,2022,146(24):e334-e482
                                                                       bladder tumor detection using deep learning[J]. Eur
               [2] 中国医师协会心血管外科分会大血管外科专业委员
                                                                       Urol,2019,76(6):714-718
                    会. 主动脉夹层诊断与治疗规范中国专家共识[J]. 中                  [16]ISENSEE F,JAEGER P F,KOHL S A A,et al. nnU⁃Net:
                    华胸心血管外科杂志,2017,33(11):641-654
                                                                       a self ⁃ configuring method for deep learning ⁃ based bio⁃
               [3] 侯凡凡. 重视造影剂所致急性肾功能衰竭的预防[J].
                                                                       medical image segmentation[J]. Nat Methods,2021,18
                    中华内科杂志,2001,40(11):723-724
                                                                      (2):203-211
               [4] MCDONALD J S,MCDONALD R J,WILLIAMSON E E,
                                                                 [17]THEODORIS C V,XIAO L,CHOPRA A,et al. Transfer
                    et al. Is intravenous administration of iodixanol associated
                                                                       learning enables predictions in network biology[J]. Na⁃
                    with increased risk of acute kidney injury,dialysis,or
                                                                       ture,2023,618(7965):616-624
                    mortality?A propensity score⁃adjusted study[J]. Radiolo⁃
                                                                 [18]DUNDAR A,GAO J,TAO A,et al. Fine detailed texture
                    gy,2017,285(2):414-424
                                                                       learning for 3D meshes with generative models[J]. IEEE
               [5] SEELIGER E,PERSSON P B. Kidney damage by iodinated
                                                                       Trans Pattern Anal Mach Intell,2023,45(12):14563-
                    contrast media[J]. Acta Physiol(Oxf),2019,227(4):  14574
                    e13259
                                                                 [19] HATA A,YANAGAWA M,YAMAGATA K,et al. Deep
               [6] FÄHLING M,SEELIGER E,PATZAK A,et al. Under⁃         learning algorithm for detection of aortic dissection on
                     standing and preventing contrast⁃induced acute kidney in⁃  non⁃contrast⁃enhanced CT[J]. Eur Radiol,2021,31(2):
                     jury[J]. Nat Rev Nephrol,2017,13(3):169-180       1151-1159
               [7] 范淑玉,张秀美,杨向红. 基层医院CT造影增强检查不                    [20] YI Y,MAO L,WANG C,et al. Advanced warning of
                    满意因素调查分析[J]. 右江民族医学院学报,2006,28                     aortic dissection on non⁃contrast CT:the combination of
                   (3):416-418                                         deep learning and morphological characteristics[J].
               [8] MATSUO Y,LECUN Y,SAHANI M,et al. Deep learning,     Front Cardiovasc Med,2021,8:762958
                    reinforcement learning,and world models[J]. Neural  [21] XIONG X,GUAN X,SUN C,et al. A cascaded deep
                    Netw,2022,152:267-275                              learning framework for detecting aortic dissection using
               [9] ZHOU S K,GREENSPAN H,DAVATZIKOS C,et al. A          non⁃contrast enhanced computed tomography[J]. Annu
                    review of deep learning in medical imaging:imaging  Int Conf IEEE Eng Med Biol Soc,2021,2021:2914-
                    traits,technology trends,case studies with progress high⁃  2917
                    lights,and future promises[J]. Proc IEEE Inst Electr  [22] HAHN L D,MISTELBAUER G,HIGASHIGAITO K,et
                    Electron Eng,2021,109(5):820-838                   al. CT⁃based true⁃ and false⁃lumen segmentation in type
               [10] CHANDRASHEKAR A,HANDA A,LAPOLLA P,et al.           B aortic dissection using machine learning[J]. Radiol
                    A deep learning approach to visualize aortic aneurysm  Cardiothorac Imaging,2020,2(3):e190179
                    morphology without the use of intravenous contrast  [23] YU Y,GAO Y,WEI J,et al. A three⁃dimensional deep
                    agents[J]. Ann Surg,2023,277(2):e449-e459          convolutional neural network for automatic segmentation
               [11]ZHOU M,LUO X,WANG X,et al. Deep learning predic⁃    and diameter measurement of type B aortic dissection[J].
                    tion for distal aortic remodeling after thoracic endovascu⁃  Korean J Radiol,2021,22(2):168-178
                    lar aortic repair in stanford type B aortic dissection[J]. J  [24] RENGIER F,WÖRZ S,GODINEZ W J,et al. Develop⁃
                    Endovasc Ther,2023:15266028231160101               ment of in vivo quantitative geometric mapping of the
               [12]HOLSTE G,OIKONOMOU E K,MORTAZAVI B J,et al.         aortic arch for advanced endovascular aortic repair:feasi⁃
                    Severe aortic stenosis detection by deep learning applied  bility and preliminary results[J]. J Vasc Interv Radiol,
                    to echocardiography[J]. Eur Heart J,2023,44(43):   2011,22(7):980-986
                    4592-4604                                    [25]STERNBERGH W C,MONEY S R,GREENBERG R K,
               [13]NISHIKIORI H,KURONUMA K,HIROTA K,et al. Deep⁃       et al. Influence of endograft oversizing on device migra⁃
   144   145   146   147   148   149   150   151   152