Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (11): 33-38.doi: 10.6040/j.issn.1671-7554.0.2020.1136

• Special topic on new progress in ophthalmic artificial intelligence • Previous Articles     Next Articles

Advances in the intelligent diagnosis of eye diseases

Yilong YIN1,*(),Xiaoming XI2   

  1. 1. Research Center of Artificial Intelligence in School of Software, Shandong University, Jinan 250101, Shandong, China
    2. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, Shandong, China
  • Received:2020-08-10 Online:2020-11-10 Published:2020-11-04
  • Contact: Yilong YIN E-mail:ylyin@sdu.edu.cn

RichHTML

14

PDF (PC)

143

Abstract:

As the number of elderly people increases, eye diseases have become a tremendous economic and societal burden. Medical imaging plays an important role in the diagnosis of eye diseases. With the development of artificial intelligence, automatic diagnosis of eye diseases with medical imaging has attracted great attention in recent years. In order to provide new insights into the intelligent diagnostics methods of eye diseases, this paper reviews the recent advances including methods based on object segmentation and eye diseases diagnosis and discusses the future work.

Key words: Eye diseases, Medical imaging, Intelligent diagnosis

CLC Number: 

  • R574
1 Kanagasingam Y , Bhuiyan A , Abràmoff MD , et al. Progress on retinal image analysis for age related macular degeneration[J]. Prog Retin Eye Res, 2014, 38: 20- 42.
doi: 10.1016/j.preteyeres.2013.10.002
2 Acott TS , Vranka JA , Keller KE , et al. Progress in retinal and eye research normal and glaucomatous outflow regulation[J]. Prog Retin Eye Res, 2020, 11: 100897.
doi: 10.1016/j.preteyeres.2020.100897
3 Ahmed SF, Walid MA, Ahmed EM, Segmentation of Choroidal Neovascularization Lesions in Fluorescein Angiograms Using Parametric Modeling of the Intensity Variation[C]. IEEE Trans Biomed Eng, 2011: 1457-1460. doi: 10.1109/TBME.2013.2237906.Epub2013Jan9.
4 Lin KS , Tsai CL , Chen SJ , et al. Automatic Evaluation of Choroidal Neovascularization in Fluorescein Angiography[J]. Advances in Intelligent Systems and Applications, 2013, 21 (2): 377- 382.
5 Tsai CL , Yang YL , Chen SJ , et al. Automatic characterization of classic choroidal neovascularization by using adaboost for supervised learning[J]. Invest Ophthalmol Vis Sci, 2011, 52 (5): 2767- 2774.
doi: 10.1167/iovs.10-6048
6 Abdelmoula WM , Shah SM , Fahmy AS . Segmentation of choroidal neovascularization in fundus fluorescein angiograms[J]. IEEE Trans Biomed Eng, 2013, 60 (5): 1439- 1445.
7 Gao SS , Liu L , Bailey ST , et al. Quantification of choroidal neovascularization vessel length using optical coherence tomography angiography[J]. J Biomed Opt, 2016, 21 (7): 76010.
doi: 10.1117/1.JBO.21.7.076010
8 Tan NM , Xu Y , Goh WB , et al. Robust multi-scale superpixel classification for optic cup localization[J]. Comput Med Imaging Graph, 2015, 40: 182- 193.
doi: 10.1016/j.compmedimag
9 Thakur N , Juneja M . Clustering based approach for segmentation of optic cup and optic disc for detection of Glaucoma[J]. Curr Med Imaging Rev, 2017, 13 (1): 99- 105.
10 Cheng J , Liu J , Xu Y , et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening[J]. IEEE Trans Med Imaging, 2013, 32 (6): 1019- 1032.
doi: 10.1109/TMI.2013.2247770
11 Girshick R , Donahue J , Darrell T , et al. Region-based convolutional networks for accurate object detection and segmentation[J]. IEEE Trans Pattern Anal Mach Intell, 2016, 38 (1): 142- 158.
doi: 10.1109/TPAMI.2015.2437384
12 Dahl GE , Yu D , Deng L , et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition[J]. IEEE Trans Audio Speech Lang Process, 2012, 20 (1): 30- 42.
13 Mohamed AR , Dahl GE , Hinton G . Acoustic modeling using deep belief networks[J]. IEEE Trans Audio Speech Lang Process, 2012, 20 (1): 14- 22.
doi: 10.1109/TASL.2011.2109382
14 Ling ZH , Deng L , Yu D , et al. Modeling spectral envelopes using restricted Boltzmann machines and deep belief networks for statistical parametric speech synthesis[J]. IEEE Trans Audio Speech Lang Process, 2013, 21 (10): 2129- 2139.
doi: 10.1109/TASL.2013.2269291
15 Zilly J , Buhmann JM , Mahapatra D . Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation[J]. Comput Med Imaging Graph, 2017, 55: 28- 41.
doi: 10.1016/j.compmedimag
16 Fu HZ , Cheng J , Xu YW , et al. Joint optic disc and cup segmentation based on mufti-label deep network and polar transformation[J]. IEEE Trans Med Imaging, 2018, 37 (7): 1597- 1605.
17 Xie Z , Ling T , Yang Y , et al. Optic disc and cup image segmentation utilizing contour-based transformation and sequence labeling networks[J]. J Med Syst, 2020, 44 (5): 1- 13.
18 Jiang YM , Duan LX , Cheng J , et al. Joint RCNN: a region-based convolutional neural network for optic disc and cup segmentation[J]. IEEE Trans Biomed Eng, 2020, 67 (2): 335- 343.
19 Yang XB , Zhang Y . Multi-atlas segmentation of optic disc in retinal images via convolutional neural network[J]. Multimed Tools Appl, 2020, 1- 11.
20 Rong YB, Yu K, Xiang DH, et al. Explaining convolutional neural networks for area estimation of choroidal neovascularization via genetic programming[M]//Computational pathology and ophthalmic medical image analysis. cham: springer international publishing, 2018: 210-218. doi: 10.1007/978-3-030-00949-6_25.
21 Maunz A , Benmansour F , Li Y , et al. Diagnostic accuracy of a machine-learning algorithm to detect and classify choroidal neovascularization based on SD-OCT in neovascular age-related macular degeneration (nAMD)[J]. Invest Ophthalmol Vis Sci, 2020, 61 (7): 2649- 2649.
22 Guan L, Yu K, Chen X. Fully automated detection and quantification of multiple retinal lesions in OCT volumes based on deep learning and improved DRLSE[C]. In Medical Imaging 2019: Image Processing. 2019: 1094933. doi: 10.1117/12.2512656.
23 Xi XM , Meng XJ , Yang L , et al. Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior[J]. Multimed Syst, 2019, 25 (2): 95- 102.
24 Gulshan V , Peng L , Coram M , et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316 (22): 2402- 2410.
doi: 10.1001/jama.2016.17216
25 Takahashi H , Tampo H , Arai Y , et al. Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy[J]. PLoS One, 2017, 12 (6): e0179790.
doi: 10.1371/journal.pone.0179790
26 Shankar K , Sait ARW , Gupta D , et al. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model[J]. Pattern Recognit Lett, 2020, 133: 210- 216.
doi: 10.1016/j.patrec.2020.02.026
27 Qummar S , Khan FG , Shah S , et al. A deep learning ensemble approach for diabetic retinopathy detection[J]. IEEE Access, 2019, 7: 150530- 150539.
doi: 10.1109/access.2019.2947484
28 de la Torre J , Valls A , Puig D . A deep learning interpretable classifier for diabetic retinopathy disease grading[J]. Neurocomputing, 2020, 396: 465- 476.
doi: 10.1016/j.neucom.2018.07.102
29 Alqudah AM . AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images[J]. Med Biol Eng Comput, 2020, 58 (1): 41- 53.
30 Fang LY , Jin YX , Huang LF , et al. Iterative fusion convolutional neural networks for classification of optical coherence tomography images[J]. J Vis Commun Image Represent, 2019, 59: 327- 333.
doi: 10.1016/j.jvcir.2019.01.022
31 Fang L , Wang C , Li S , et al. Attention to lesion: lesion-aware convolutional neural network for retinal optical coherence tomography image classification[J]. IEEE Trans Med Imaging, 2019, 38 (8): 1959- 1970.
doi: 10.1109/TMI.2019.2898414
32 Das V , Prabhakararao E , Dandapat S , et al. B-scan attentive CNN for the classification of retinal optical coherence tomography volumes[J]. IEEE Signal Process Lett, 2020, 27: 1025- 1029.
doi: 10.1109/LSP.2020.3000933
33 Abbas Q . Glaucoma-deep: detection of Glaucoma eye disease on retinal fundus images using deep learning[J]. Ijacsa, 2017, 8 (6): 41- 45.
34 Diaz-Pinto A , Morales S , Naranjo V , et al. Cnns for automatic glaucoma assessment using fundus images: an extensive validation[J]. BioMed Eng OnLine, 2019, 18: 29.
doi: 10.1186/s12938-019-0649-y
35 Fu HZ , Cheng J , Xu YW , et al. Disc-aware ensemble network for glaucoma screening from fundus image[J]. IEEE Trans Med Imaging, 2018, 37 (11): 2493- 2501.
doi: 10.1109/TMI.2018.2837012
36 Petersen CA , Mehta P , Lee AY , et al. Data-driven, feature-agnostic deep learning vs retinal nerve fiber layer thickness for the diagnosis of glaucoma[J]. JAMA Ophthalmol, 2020, 138 (4): 339- 340.
37 Chai YD , Liu HY , Xu J . Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models[J]. Knowl-Based Syst, 2018, 161: 147- 156.
doi: 10.1016/j.knosys.2018.07.043
38 Xu X , Zhang LL , Li JQ , et al. A hybrid global -local representation CNN model for automatic cataract grading[J]. IEEE J Biomed Health Inform, 2020, 24 (2): 556- 567.
39 Long EP , Lin HT , Liu ZZ , et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts[J]. Nat Biomed Eng, 2017, 1 (2): 0024.
40 Gao XT , Lin S , Wong TY . Automatic feature learning to grade nuclear cataracts based on deep learning[J]. Comput Vis--ACCV 2014, 2015, 2693- 2701.
doi: 10.1007/978-3-319-16808-1_42
41 Zhang H , Niu K , Xiong Y , et al. Automatic cataract grading methods based on deep learning[J]. Comput Methods Programs Biomed, 2019, 182: 104978.
doi: 10.1016/j.cmpb.2019.07.006
42 Zhou Y , Li GQ , Li HQ . Automatic cataract classification using deep neural network with discrete state transition[J]. IEEE Trans Med Imaging, 2020, 39 (2): 436- 446.
doi: 10.1109/TMI.2019.2928229
[1] Yilong YIN,Xiaoming XI,Xianjing MENG. Intelligent diagnosis methods of Alzheimer's disease [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 14-21.
[2] Haotian LIN,Longhui LI,Jingjing CHEN. Research progress of artificial intelligence in childhood eye diseases [J]. Journal of Shandong University (Health Sciences), 2020, 58(11): 11-16.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] XIAO Juan, XIAO Qiang, CONG Wei, LI Ting, DING Shouluan, ZHANG Yuan, SHAO Chunchun, WU Mei, LIU Jianing, JIA Hongying. Comparison of diagnostic efficacy of two kinds of thyroid imagine reporting and data systems[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 53 -59 .
[2] DING Xiangyun, YU Qingmei, ZHANG Wenfang, ZHUANG Yuan, HAO Jing. Correlation of the expression of insulin-like growth factor II in granulosa cells and ovulation induction outcomes of 84 patients with polycystic ovary syndrome[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 60 -66 .
[3] XU Jixi, CHEN Weijian. Diffuse midline glioma with H3 K27M mutation in the spinal cord: a case report[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 96 -101 .
[4] JI Yongjuan, XIANG Zini, KUANG Guifang. A path analysis on the influence of burnout on quality of life among staff in two tertiary hospitals in Qingdao[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 102 -107 .
[5] DENG Qingwen, LIU Wenbin. Multilevel analysis of health related quality of life of patients with cardiovascular disease and its determinants[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 115 -121 .
[6] Chuanzhu YAN,Wei WANG,Kunqian JI,Yuying ZHAO. Mitochondrial dysfunction and related brain diseases[J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 34 -41 .
[7] WU Mengtao, WU Peng, YANG Yanfei, TANG Dianjun, MIAO Xiangling, LI Fandong. Application of CT venography in the diagnosis and treatment of recurrent lower extremity varicose veins[J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 21 -26 .
[8] LIU Chao, YAN Dong, LI Xiang, ZHANG Lei, LI Yan. Clinical analysis of laparoscopy assisted small incision of umbilicus in the treatment of Meckels diverticulum in children[J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 40 -44 .
[9] MENG Zhaotun, LI Qin, SUN Hui, WU Xinfang, SUN Wenkai, GAO Wei. Correlation between interleukin-18 single nucleotide polymorphism and susceptibility of laryngeal squamous cell carcinoma: a report of 73 cases[J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 58 -63 .
[10] AN Di, WANG Xinting, LIU Zhenzhen, JIA Cunxian. Association between restless legs syndrome and suicidal behaviors among adolescents[J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 77 -82 .
Copyright 2018 © Editorial office of Journal of Shandong University (Health Sciences)
Supported by Beijing Magtech Co.Ltd