| [1] |
Wallis C. How artificial intelligence will change medicine[J]. Nature, 2019, 576(7787): S48.
|
| [2] |
Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology[J]. British Journal of Ophthalmology, 2019, 103(2): 167-175.
|
| [3] |
Savoy M. Diagnostic tests what physicians need to know IDx-DR for diabetic retinopathy screening margot savoy[J]. American Family Physician, 2020, 101(5): 307-308.
|
| [4] |
Liu Y, Yang J, Zhou Y, et al. Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network[J]. British Journal of Ophthalmology, 2020: e315338.
|
| [5] |
Wood EH, Korot E, Storey PP, et al. The retina revolution: signaling pathway therapies, genetic therapies, mitochondrial therapies, artificial intelligence[J]. Current Opinion in Ophthalmology, 2020, 31(3): 207-214.
|
| [6] |
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[M]. New York: Nature Publishing Group, 2018: 1342.
|
| [7] |
Moccia S, De Momi E, El Hadji S, et al. Blood vessel segmentation algorithms-review of methods, datasets and evaluation metrics[J]. Computer Methods and Programs in Biomedicine, 2018, 158: 71-91.
|
| [8] |
Schlegl T, Waldstein SM, Bogunovic H, et al. Fully automated detection and quantification of macular fluid in OCT using deep learning[J]. Ophthalmology, 2018, 125(4): 549-558.
|
| [9] |
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.
|
| [10] |
Shih FY, Patel H. Deep learning classification on optical coherence tomography retina images[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2020, 34(8): 59-68.
|
| [11] |
Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis[J]. The Lancet Digital Health, 2019, 1(6): e271-e297.
|
| [12] |
Pandey SK, Sharma V. Robotics and ophthalmology: Are we there yet？[J]. Indian Journal of Ophthalmology, 2019, 67(7): 988-994.
|
| [13] |
Bourcier T, Chammas J, Gaucher D, et al. Robot-assisted simulated strabismus surgery[J]. Translational Vision Science & Technology, 2019, 8(3): e26.
|
| [14] |
Edwards TL, Xue K, Meenink HCM, et al. First-in-human study of the safety and viability of intraocular robotic surgery[J]. Nature Biomedical Engineering, 2018, 2(9): 649-656.
|
| [15] |
Song P, Du Y, Chan KY, et al. The national and subnational prevalence and burden of age-related macular degeneration in China[J]. Journal of Global Health, 2017, 7(2): e020703.
|
| [16] |
National Eye Institute. Age-Related Macular Degeneration (AMD) Data and Statistics[EB/OL]．[2020-04-23].
URL
|
| [17] |
Burlina PM, Joshi N, Pekala M, et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks[J]. JAMA Ophthalmology, 2017, 135(11): 1170-1176.
|
| [18] |
Gonzalez-Gonzalo C, Sanchez-Gutierrez V, Hernandez-Martinez P, et al. Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration[J]. Acta Ophthalmologica, 2020, 98(4): 368-377.
|
| [19] |
Venhuizen FG, Van Ginneken B, Van Asten F, et al. Automated staging of age-related macular degeneration using optical coherence tomography[J]. Investigative Ophthalmology & Visual Science, 2017, 58(4): 2318-2328.
|
| [20] |
Treder M, Lauermann JL, Eter N. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning[J]. Graefes Archive for Clinical and Experimental Ophthalmology, 2018, 256(2): 259-265.
|
| [21] |
Sun Z, Sun Y. Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images[J]. Journal of Biomedical Optics, 2019, 24(5): e056003.
|
| [22] |
Heo TY, Kim KM, Min HK, et al. Development of a deep-learning-based artificial intelligence tool for differential diagnosis between dry and neovascular age-related macular degeneration[J]. Diagnostics, 2020, 10(5): e261.
|
| [23] |
De Sisternes L, Jonna G, Greven MA, et al. Individual drusen segmentation and repeatability and reproducibility of their automated quantification in optical coherence tomography images[J]. Translational Vision Science & Technology, 2017, 6(1): 12.
|
| [24] |
Schmidt-Erfurth U, Waldstein SM, Klimscha S, et al. Prediction of individual disease conversion in early AMD using artificial intelligence[J]. Investigative Ophthalmology & Visual Science, 2018, 59(8): 3199-3208.
|
| [25] |
Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, et al. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration[J]. Ophthalmology Retina, 2018, 2(1): 24-30.
|
| [26] |
Rohm M, Tresp V, Müller M, et al. Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration[J]. Ophthalmology, 2018, 125(7): 1028-1036.
|
| [27] |
Song P, Yu J, Chan KY, et al. Prevalence, risk factors and burden of diabetic retinopathy in China: a systematic review and meta-analysis[J]. Journal of Global Health, 2018, 8(1): e010803.
|
| [28] |
Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology[J]. Survey of Ophthalmology, 2019, 64(2): 233-240.
|
| [29] |
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.
|
| [30] |
FDA. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems[EB/OL]. [2020-08-11].
URL
|
| [31] |
Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning[J]. Ophthalmology, 2017, 124(7): 962-969.
|
| [32] |
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.
|
| [33] |
Natarajan S, Jain A, Krishnan R, et al. Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone[J]. JAMA Ophthalmology, 2019, 137(10): 1182-1188.
|
| [34] |
Lahmiri S. Hybrid deep learning convolutional neural networks and optimal nonlinear support vector machine to detect presence of hemorrhage in retina[J]. Biomedical Signal Processing and Control, 2020, 60: e101978.
|
| [35] |
Gerendas BS, Bogunovic H, Sadeghipour A, et al. Computational image analysis for prognosis determination in DME[J]. Vision Research, 2017, 139: 204-210.
|
| [36] |
Musacchio N, Giancaterini A, Guaita G, et al. Artificial intelligence and big data in diabetes care: a position statement of the italian association of medical diabetologists[J]. Journal of Medical Internet Research, 2020, 22(6): e16922.
|
| [37] |
Gilbert C, Malik ANJ, Nahar N, et al. Epidemiology of ROP update——Africa is the new frontier[J]. Seminars in Perinatology, 2019, 43(6): 317-322.
|
| [38] |
Xiao S, Bucher F, Wu Y, et al. Fully automated, deep learning segmentation of oxygen-induced retinopathy images[J]. JCI insight, 2017, 2(24): e97585.
|
| [39] |
Mazzaferri J, Larrivée B, Cakir B, et al. A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model[J]. Scientific Reports, 2018, 8(1): 3916.
|
| [40] |
Ataer-Cansizoglu E, Bolon-Canedo V, Campbell JP, et al. Computer-based image analysis for plus disease diagnosis in retinopathy of prematurity: performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis[J]. Translational Vision Science & Technology, 2015, 4(6): e5.
|
| [41] |
Brown JM, Campbell JP, Beers A, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks[J]. JAMA Ophthalmology, 2018, 136(7): 803-810.
|
| [42] |
Rogers S, Mcintosh RL, Cheung N, et al. The prevalence of retinal vein occlusion: pooled data from population studies from the United States, Europe, Asia, and Australia[J]. Ophthalmology, 2010, 117(2): 313-319.
|
| [43] |
Nagasato D, Tabuchi H, Ohsugi H, et al. Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion[J]. International Journal of Ophthalmology, 2019, 12(1): 94-99.
|
| [44] |
Seebock P, Orlando JI, Schlegl T, et al. Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT[J]. IEEE Transactions on Medical Imaging, 2020, 39(1): 87-98.
|
| [45] |
Vogl WD, Waldstein SM, Gerendas BS, et al. Analyzing and predicting visual acuity outcomes of anti-VEGF therapy by a longitudinal mixed effects model of imaging and clinical data[J]. Investigative Ophthalmology & Visual Science, 2017, 58(10): 4173-4181.
|
| [46] |
Vogl WD, Waldstein SM, Gerendas BS, et al. Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images[J]. IEEE Transactions on Medical Imaging, 2017, 36(9): 1773-1783.
|
| [47] |
Lu W, Tong Y, Yu Y, et al. Applications of artificial intelligence in ophthalmology: general overview[J]. Journal of Ophthalmology, 2018：5278196.
|
| [48] |
Samuel AL. Some studies in machine learning using the game of checkers[J]. IBM Journal of Research and Development, 1959, 3(3): 210-229.
|
| [49] |
Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
|
| [50] |
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Advances in neural information processing systems 27[M]. New York: Curran Associates, Inc., 2014.
|
| [51] |
Ko F, Foster PJ, Strouthidis NG, et al. Associations with retinal pigment epithelium thickness measures in a large cohort: results from the UK Biobank[J]. Ophthalmology, 2017, 124(1): 105-117.
|
| [52] |
Keane PA, Grossi CM, Foster PJ, et al. Optical coherence tomography in the UK biobank study-rapid automated analysis of retinal thickness for large population-based studies[J]. PloS One, 2016, 11(10): e0164095.
|
| [53] |
Baylor E, Beede E, Hersch F, et al. A Human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy[C]. Honolulu, HI, USA: Human Factors in Computing Systems (CHI′20), 2020: 1-12.
|
| [54] |
Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina[J]. Progress in Retinal and Eye Research, 2018, 67: 1-29.
|
| [55] |
Balthazar P, Harri P, Prater A, et al. Protecting your patients′ interests in the era of big data, artificial intelligence, and predictive analytics[J]. Journal of the American College of Radiology, 2018, 15(3 Pt B): 580-586.
|
| [56] |
Freedman DH. Hunting for new drugs with AI[J]. Nature, 2019, 576(7787): S49-S53.
|
| [57] |
Wolk S, Kleemann M, Reeps C. Artificial intelligence in vascular surgery and vascular medicine[J]. Chirurg, 2020, 91(3): 195-200.
|
| [58] |
中共中央国务院. "健康中国2030"规划纲要[EB/OL]．[2020-08-11].
URL
|
| [59] |
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare[J]. Nature Medicine, 2019, 25(1): 24-29.
|
| [60] |
Heidary F, Gharebaghi R. Ideas to assist the underprivileged dispossessed individuals[J]. Medical Hypothesis, 2012, 1(3): 43-44.
|