您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (11): 24-32, 38.doi: 10.6040/j.issn.1671-7554.0.2020.1249

• 眼科人工智能新进展专题 • 上一篇    下一篇

青光眼影像人工智能深度学习研究现状与展望

CheungCarol Y.*(),冉安然   

  1. 香港中文大学眼科与视觉科学系，香港 999077
  • 收稿日期:2020-09-04 出版日期:2020-11-10 发布日期:2020-11-04
  • 通讯作者: CheungCarol Y. E-mail:carolcheung@cuhk.edu.hk
  • 作者简介:Carol Y. Cheung,香港中文大学眼科及视觉科学系助理教授，博士研究生导师。主要研究领域是“眼部成像”，其理论基础为眼睛是人体循环系统和神经系统的“窗口”。在香港和新加坡有10多年从事眼部成像研究的经验，致力于开发和应用图像分析以及人工智能技术对糖尿病性视网膜病变、青光眼和阿尔茨海默病的研究|目前从事多项研究，包括开发和应用新型成像技术，以及探索如何利用这些技术改善临床工作流程和公共卫生。为人们理解和认识主要眼病以及脑部疾病提供新的角度，促进用先进的眼部成像技术进行更有针对性、有效的疾病筛查，实现防盲和脑部疾病的早期检测。在国际索引的同行评审期刊发表200多篇有影响力的论文，并撰写10本书的相关章节，专注于与视网膜、脑部疾病有关的视网膜成像技术|国际科学期刊和国际资助基金会的定期审稿人，亚太眼科影像学会秘书长，亚太远程眼科学会理事会理事兼财务主管,海峡两岸医学与健康交流协会视网膜血管疾病委员会的名誉会员，以及中国医学教育协会智能医学专业委员会智能眼科学组的理事会成员

Artificial intelligence deep learning in glaucoma imaging: current progress and future prospect

Carol Y. Cheung*(),Anran RAN   

  1. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
  • Received:2020-09-04 Online:2020-11-10 Published:2020-11-04
  • Contact: Carol Y. Cheung E-mail:carolcheung@cuhk.edu.hk

RichHTML

70

PDF (PC)

388

摘要：

青光眼是一组异质性神经退行性疾病，其特征是视网膜神经节细胞及其轴突逐渐消失，现已成为全球不可逆性失明的主要原因。人工智能(AI)是由机器展示的智能，而深度学习(DL)是其中一个基于深度神经网络的分支，在医学成像领域取得了重大突破。在青光眼影像方面，已有越来越多的研究将DL应用于眼底图像以及光学相干断层扫描(OCT)，以检测青光眼性视神经病变。有很好的结果显示，将DL技术整合到影像中进行青光眼评估是高效、准确的，这可能会解决当前实践和临床工作流程中的一些难题。但是，未来进一步的研究对于解决现存挑战至关重要，例如为不同研究之间的图像标记建立标准，将“黑匣子”的学习过程进行可视化，提高模型在未知数据集上的泛化能力，开发基于DL的实际应用程序，以及建立合理的临床工作流程，进行前瞻性验证和成本效益分析等。本文总结了AI应用于青光眼影像的最新研究现状，并讨论了对临床的潜在影响和未来的研究方向。

关键词: 青光眼影像, 人工智能, 光学相干断层扫描, 眼底照相, 深度学习

Abstract:

Glaucoma is a group of heterogeneous neurodegenerative diseases, characterized by the gradual loss of retinal ganglion cells and their axons, and has now become the major reason of irreversible blindness worldwide. Artificial intelligence (AI) is intelligence demonstrated by machines. Deep learning (DL) is a subset of AI based on deep neural networks, and it has made great breakthroughs in medical imaging. In glaucoma imaging, research interests have been increasing on applying DL in fundus photographs and optical coherence tomography (OCT) for glaucomatous optic neuropathy (GON) detection. Promising results show that the incorporation of DL technology in imaging for glaucoma assessment is efficient and accurate, which could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as setting a standard for ground truth labelling among different studies, visualizing the learning process in the "black box", improving the model generalizability on unseen datasets, developing the DL-powered infrastructure for real-world implementation, establishing a practical clinical workflow, conducting prospective validation and cost-effectiveness analysis. This review summarizes recent studies on the application of AI on glaucoma imaging, discusses the potential clinical impact and future research directions.

Key words: Glaucoma imaging, Artificial intelligence, Optical coherence tomography, Fundus photography, Deep learning

中图分类号: 

  • R574

图1

人工智能、机器学习以及深度学习"

表1

DL模型开发与性能评估的基本数据设置"

术语 说明
训练集 与调试集和内部验证集来自同一个大的数据集，是基于一定比例划分且互不重合，用于建立深度学习模型。研究者可构建具有不同参数甚至不同网络架构的DL模型来学习训练集中青光眼性视神经改变的相关特征。
调试集 与训练集和内部验证集来自同一个大的数据集，是基于一定比例划分且互不重合，用于实时评估不同模型在训练期间的性能。研究者可根据实时观察的学习曲线进行参数调节，确定训练停止节点，并最终选择最佳DL模型。
内部测试集 与训练集和调试集来自同一个大的数据集，是基于一定比例划分且互不重合，用于评估所选的最佳DL模型在该未知数据集上的性能。临床研究中，研究者需汇报模型在内部测试集上的性能，通常包括受试者工作特征曲线下面积，灵敏度，特异性以及准确性等。
外部测试集 一个或多个独立的数据集，与训练集、测试集和内部验证集来自不同数据集，用于评估上述DL模型在其他未知数据集上的性能，验证其临床通用性。研究者同样需汇报模受试者工作特征曲线下面积、灵敏度、特异性以及准确性等。当DL模型在内部以及外部测试集都具有较好性能时，即证明该模型有良好的通用性。
K-倍交叉验证 主要用于数据量有限时。将整个大的数据集分为k个相等的小数据集，其中(k-1)个合并为训练集用于模型开发，剩下的一个作为测试集评估模型性能。该训练过程重复k次，每次选择其中一个小数据集用作测试集。研究者应汇报所有测试集的平均性能，通常为${\bar x}$±s。
留一法交叉验证 主要用于数据量非常有限时，是一种特殊类型的K-倍交叉验证，此时K等于数据总数(即标注图像的总数)。仅留下一张图像来测试模型性能，其他图像全部用于训练。该训练过程同样重复k次，研究者应汇报所有测试集的平均性能，通常为${\bar x}$±s。

图2

DL模型的开发与性能评估基础步骤"

图3

基于光学相干断层扫描(optical coherence tomography, OCT)不同类型输入的DL模型 A：传统OCT报告；B:视盘区未分割二维扫描图；C：视盘区立体扫描图。"

图4

人工智能青光眼影像分析系统在临床中辅助青光眼检测或筛查的工作流程"

1 Weinreb RN , Aung T , Medeiros FA . The pathophysiology and treatment of glaucoma: a review[J]. JAMA, 2014, 311 (18): 1901- 1911.
doi: 10.1001/jama.2014.3192
2 Tham YC , Li X , Wong TY , et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis[J]. Ophthalmology, 2014, 121 (11): 2081- 2090.
doi: 10.1016/j.ophtha.2014.05.013
3 Weinreb RN , Leung CKS , Crowston JG , et al. Primary open-angle glaucoma[J]. Nat Rev Dis Primers, 2016, 2: 16067.
doi: 10.1016/S0140-6736(04)16257-0
4 Jonas JB , Aung T , Bourne RR , et al. Glaucoma[J]. Lancet, 2017, 390 (10108): 2183- 2193.
doi: 10.1016/S0140-6736(17)31469-1
5 Momont AC , Mills RP . Glaucoma screening: current perspectives and future directions[J]. Semin Ophthalmol, 2013, 28 (3): 185- 190.
doi: 10.3109/08820538.2013.771200
6 Schuman JS . Detection and diagnosis of glaucoma: ocular imaging[J]. Invest Ophthalmol Vis Sci, 2012, 53 (5): 2488- 2490.
doi: 10.1167/iovs.12-9483k
7 Rossetto JD , Melo LAS , Campos M , et al. Agreement on the evaluation of glaucomatous optic nerve head findings by ophthalmology residents and a glaucoma specialist[J]. Clin Ophthalmol, 2017, 11: 1281- 1284.
doi: 10.2147/OPTH.S140225
8 Cheung CY , Leung CK , Lin D , et al. Relationship between retinal nerve fiber layer measurement and signal strength in optical coherence tomography[J]. Ophthalmology, 2008, 115 (8): 1347- 1351.
doi: 10.1016/j.ophtha.2007.11.027
9 Cheung CY , Chan N , Leung CK . Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: impact of signal strength on analysis of the RNFL map[J]. Asia Pac J Ophthalmol (Phila), 2012, 1 (1): 19- 23.
doi: 10.1097/APO.0b013e31823e595d
10 Biswas S , Lin C , Leung CK . Evaluation of a myopic normative database for analysis of retinal nerve fiber layer thickness[J]. JAMA Ophthalmol, 2016, 134 (9): 1032- 1039.
doi: 10.1001/jamaophthalmol.2016.2343
11 Andresen SL . John McCarthy: father of AI[J]. IEEE Intell Syst, 2002, 17 (5): 84- 85.
12 Simon A DMS , Venkatesan S , Babu DRR . An overview of machine learning and its applications[J]. International Journal of Electrical Sciences & Engineering, 2015, 1 (1): 22- 24.
13 Shinde PP , Shah S . A review of machine learning and deep learning applications[J]. 2018 Fourth International Conference on Computing Communication Control and Automation (Iccubea), Pune, India, 2018, 1- 8.
doi: 10.1109/ICCUBEA.2018.8697857
14 Aggarwal CC . Training deep neural networks[M]. Cham: Springer International Publishing, 2018: 105- 167.
doi: 10.1007/978-3-319-94463-0_3
15 Becker AS , Mueller M , Stoffel E , et al. Classification of breast cancer form ultrasound imaging using a generic deep learning analysis software: a pilot study[J]. Br J Radiol, 2017, 20170576.
doi: 10.1259/bjr.20170576
16 Walsh SLF , Calandriello L , Silva M , et al. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study[J]. Lancet Respir Med, 2018, 6 (11): 837- 845.
doi: 10.1016/S2213-2600(18)30286-8
17 Brinker TJ , Hekler A , Enk A , et al. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task[J]. Eur J Cancer, 2019, 111: 148- 154.
doi: 10.1016/j.ejca.2019.02.005
18 de Fauw J , Ledsam JR , Romera-Paredes B , et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nat Med, 2018, 24 (9): 1342- 1350.
doi: 10.1038/s41591-018-0107-6
19 Ting DSW , Cheung CY , Lim G , et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes[J]. JAMA, 2017, 318 (22): 2211- 2223.
doi: 10.1001/jama.2017.18152
20 Kermany DS , Goldbaum M , Cai W , et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172 (5): 1122- 1131.
doi: 10.1016/j.cell.2018.02.010
21 Hekler A , Utikal JS , Enk AH , et al. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images[J]. Eur J Cancer, 2019, 118: 91- 96.
doi: 10.1016/j.ejca.2019.06.012
22 Maron RC , Weichenthal M , Utikal JS , et al. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks[J]. Eur J Cancer, 2019, 119: 57- 65.
doi: 10.1016/j.ejca.2019.06.013
23 Dascalu A , David EO . Skin cancer detection by deep learning and sound analysis algorithms: a prospective clinical study of an elementary dermoscope[J]. EBioMedicine, 2019, 43: 107- 113.
doi: 10.1016/j.ebiom.2019.04.055
24 Al-Antari MA , Al-Masni MA , Kim TS . Deep learning computer-aided diagnosis for breast Lesion in digital mammogram[J]. Adv Exp Med Biol, 2020, 1213: 59- 72.
doi: 10.1007/978-3-030-33128-3_4
25 O'Mahony N , Campbell S , Carvalho A , et al. Deep learning vs. traditional computer vision[J]. Adv Intell Syst, 2020, 943: 128- 144.
26 Wang JJ , Ma YL , Zhang LB , et al. Deep learning for smart manufacturing: Methods and applications[J]. J Manuf Syst, 2018, 48: 144- 156.
doi: 10.1016/j.jmsy.2018.01.003
27 Rawat W , Wang Z . Deep convolutional neural networks for image classification: a comprehensive review[J]. Neural Comput, 2017, 29 (9): 2352- 449.
doi: 10.1162/neco_a_00990
28 Aggarwal CC. Convolutional Neural Networks. In: Neural Networks and Deep Learning[J]. Springer Cham, 2018. doi: 10.1007/978-3-319-94463-0_8.
29 Krizhevsky A , Sutskever I , Hinton GE . ImageNet classification with deep convolutional neural networks[J]. Commun ACM, 2017, 60 (6): 84- 90.
doi: 10.1145/3065386
30 LeCun Y , Bengio Y , Hinton G . Deep learning[J]. Nature, 2015, 521 (7553): 436- 444.
doi: 10.1038/nature14539
31 Benuwa B , Zhan YZ , Ghansah B , et al. A review of deep machine learning[J]. Int J Eng Res Afr, 2016, 24: 124- 136.
doi: 10.4028/www.scientific.net/JERA.24.124
32 Ying X . An overview of overfitting and its solutions[J]. 2018 International conference on computer information science and application technology, 2019, 1168.
doi: 10.1088/1742-6596/1168/2/022022
33 Aggarwal CC. Teaching deep learners to generalize. in: neural networks and deep learning[J]. Springer, Cham, 2018. doi: 10.1007/978-3-319-94463-0_4.
34 Zilly J , Buhmann JM , Mahapatra D . Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation[J]. Comput Med Imag Grap, 2017, 55: 28- 41.
doi: 10.1016/j.compmedimag.2016.07.012
35 Yu S , Xiao D , Frost S , et al. Robust optic disc and cup segmentation with deep learning for Glaucoma detection[J]. Comput Med Imaging Graph, 2019, 74: 61- 71.
doi: 10.1016/j.compmedimag
36 Sevastopolsky A . Optic disc and cup segmentation methods for Glaucoma detection with modification of U-Net convolutional neural network[J]. Pattern Recognit Image Anal, 2017, 27 (3): 618- 624.
doi: 10.1134/S1054661817030269
37 Fu H , Cheng J , Xu Y , et al. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation[J]. IEEE Trans Med Imaging, 2018, 37 (7): 1597- 1605.
doi: 10.1109/TMI.2018.2791488
38 Li Z , He Y , Keel S , et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs[J]. Ophthalmology, 2018, 125 (8): 1199- 1206.
doi: 10.1016/j.ophtha.2018.01.023
39 Liu H , Li L , Michael Wormstone I , et al. Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs[J]. JAMA Ophthalmol, 2019, 137 (12): 1353- 1360.
doi: 10.1001/jamaophthalmol.2019.3501
40 Medeiros FA , Jammal AA , Thompson AC . From machine to machine: an oct-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs[J]. Ophthalmology, 2019, 126 (4): 513- 521.
doi: 10.1016/j.ophtha.2018.12.033
41 Thompson AC , Jammal AA , Medeiros FA . A deep learning algorithm to quantify neuroretinal rim loss from optic disc photographs[J]. Am J Ophthalmol, 2019, 201: 9- 18.
doi: 10.1016/j.ajo
42 Asaoka R , Murata H , Hirasawa K , et al. Using Deep Learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images[J]. Am J Ophthalmol, 2019, 198: 136- 145.
doi: 10.1016/j.ajo
43 Yosinski J , Clune J , Bengio Y , et al. How transferable are features in deep neural networks?[J]. Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014, (2): 3320- 3328.
doi: 10.5555/2969033.2969197
44 Lee J , Kim YK , Park KH , et al. Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier[J]. Journal of Glaucoma, 2020, 29 (4): 287- 294.
doi: 10.1097/IJG.0000000000001458
45 Thompson AC , Jammal AA , Berchuck SI , et al. Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans[J]. JAMA Ophthalmol, 2020, 138 (4): 333- 339.
doi: 10.1001/jamaophthalmol.2019.5983
46 Wang X , Chen H , Ran AR , et al. Towards multi-center Glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning[J]. Med Image Anal, 2020, 63: 101695.
doi: 10.1016/j.media
47 Maetschke S , Antony B , Ishikawa H , et al. A feature agnostic approach for glaucoma detection in OCT volumes[J]. PLoS One, 2019, 14 (7): e0219126.
doi: 10.1371/journal.pone.0219126
48 Ran AR , Cheung CY , Wang X , et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis[J]. Lancet Digit Heal, 2019, 1 (4): 172- 182.
doi: 10.1016/S2589-7500(19)30085-8
49 Badano A , Graff CG , Badal A , et al. Evaluation of digital breast tomosynthesis as replacement of full-field digital mammography using an in silico imaging trial[J]. Jama Netw Open, 2018, 1 (7): e185474.
doi: 10.1001/jamanetworkopen.2018.5474
50 Cha KH , Petrick N , Pezeshk A , et al. Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images[J]. Proc SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095004..
doi: 10.1117/12.2512604
51 Goodfellow IJ , Pouget-Abadie J , Mirza M , et al. Generative Adversarial Nets[J]. Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014, (2): 2672- 2680.
doi: 10.5555/2969033.2969125
52 Sun Y , Zhou CF , Fu YW , et al. Parasitic Gan for Semi-Supervised Brain Tumor Segmentation[J]. Ieee Image Proc, 2019, 1535- 1539.
doi: 10.1109/ICIP.2019.8803073
53 Yang Y , Nan F , Yang P , et al. GAN-Based Semi-Supervised Learning Approach for Clinical Decision Support in Health-IoT Platform[J]. IEEE Access, 2019, 7: 8048- 8057.
doi: 10.1109/ACCESS
54 Wang X , Tang FY , Chen H , et al. UD-MIL: Uncertainty-driven deep multiple instance learning for OCT image classification[J]. IEEE J Biomed Health Inform, 2020, 1.
doi: 10.1109/jbhi
55 Fu HZ , Li F , Sun X , et al. AGE challenge- angle closure glaucoma evaluation in anterior segment optical coherence tomography[J]. Medical Image Analysis, 2020, (66): 101798.
doi: 10.1016/j.media.2020.101798
56 Mariottoni E, Datta S, Dov D, et al. Artificial intelligence mapping of structure to function in glaucoma[J]. 2020, 9(2): 19.
57 Yu S , Xiao D , Frost Shaun , et al. Robust optic disc and cup segmentation with deep learning for glaucoma detection[J]. Computerized Medical Imaging Analysis Graphics, 2019, (74): 61- 71.
doi: 10.1016/j.compmedimag
58 Tan NYQ , Friedman DS , Stalmans I , et al. Glaucoma screening: where are we and where do we need to go?[J]. Curr Opin Ophthalmol, 2020, 31 (2): 91- 100.
doi: 10.1097/ICU.0000000000000649
[1] 刘琚,吴强,于璐跃,林枫茗. 基于深度学习的脑肿瘤图像分割[J]. 山东大学学报 (医学版), 2020, 1(8): 42-49, 73.
[2] 李新钢,张鑫,陈安静. 当代脑计划研究进展[J]. 山东大学学报 (医学版), 2020, 1(8): 5-9, 21.
[3] 赵继宗. 神经外科学是脑科学研究的一支主力军[J]. 山东大学学报 (医学版), 2020, 1(8): 1-4.
[4] 曲毅,张焕开,宋先,初宝睿. 人工智能诊断系统在视网膜疾病的研究进展[J]. 山东大学学报 (医学版), 2020, 58(11): 39-44.
[5] 戈宗元,贺婉佶,琚烈,姚轩,王璘,黄烨霖,杨志文,熊健皓,包怡宁,李明,张兵,赵昕. 眼科人工智能的算法新进展[J]. 山东大学学报 (医学版), 2020, 58(11): 17-23.
[6] 林浩添,李龙辉,陈睛晶. 儿童眼病的人工智能研究进展[J]. 山东大学学报 (医学版), 2020, 58(11): 11-16.
[7] 何明光,刘驰,李治玺. 人工智能在眼科真实临床场景的应用：机遇和挑战[J]. 山东大学学报 (医学版), 2020, 58(11): 1-10.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 索东阳,申飞,郭皓,刘力畅,杨惠敏,杨向东. Tim-3在药物性急性肾损伤动物模型中的表达及作用机制[J]. 山东大学学报 (医学版), 2020, 1(7): 1 -6 .
[2] 马青源,蒲沛东,韩飞,王超,朱洲均,王维山,史晨辉. miR-27b-3p调控SMAD1对骨肉瘤细胞增殖、迁移和侵袭作用的影响[J]. 山东大学学报 (医学版), 2020, 1(7): 32 -37 .
[3] 李宁,李娟,谢艳,李培龙,王允山,杜鲁涛,王传新. 长链非编码RNA AL109955.1在80例结直肠癌组织中的表达及对细胞增殖与迁移侵袭的影响[J]. 山东大学学报 (医学版), 2020, 1(7): 38 -46 .
[4] 徐玉香,刘煜东,张蓬,段瑞生. 101例脑小血管病患者脑微出血危险因素的回顾性分析[J]. 山东大学学报 (医学版), 2020, 1(7): 67 -71 .
[5] 罗昕,何兵,聂清生,侯震波,董军,李玉花,曾祥芹,刘伟,孔德民,曹金凤. 磁共振扩散加权成像单指数模型与扩散峰度成像模型在61例肾透明细胞癌分级中的对比[J]. 山东大学学报 (医学版), 2020, 1(7): 89 -95 .
[6] 张迪,于猛,刘霞. 神经调控技术简述[J]. 山东大学学报 (医学版), 2020, 1(8): 50 -60 .
[7] 张伟,谭文浩,李贻斌. 基于深度强化学习的四足机器人运动控制发展现状与展望[J]. 山东大学学报 (医学版), 2020, 1(8): 61 -66 .
[8] 陈安静,张训. 靶向小类泛素化修饰的胶质瘤治疗新策略[J]. 山东大学学报 (医学版), 2020, 1(8): 88 -94 .
[9] 张洪彬,赵寒辉,王素霞,周鹏,贺青卿,王延群,丁伟平,柳刚. 303例甲状旁腺切除术围术期观察及术后严重低钙血症危险因素分析[J]. 山东大学学报 (医学版), 2020, 1(9): 14 -20 .
[10] 孙薏丰,高玉,梁永媛,高杨. CPLX2在30例肝癌组织的表达及其对体外细胞增殖与侵袭的影响[J]. 山东大学学报 (医学版), 2020, 1(9): 34 -39 .
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发