Application and prospect of artificial intelligence in the analysis of fundus images of pathological myopia_Chinese Journal of Ocular Fundus Diseases

Authors：

 Xu Xun , Yu Qi

Department of Ophthalmology, The First People’s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai 200080, China;

Corresponding author：

Xu Xun, Email: drxuxun@sjtu.edu.cn

Keywords：

Myopia, degenerative; Artificial intelligence; Editorial

DOI：

10.3760/cma.j.issn.1005-1015.2019.05.001

Video：

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Pathological myopia is one of the most challenging clinical diseases in the field of ophthalmology. The accurate definition, standard classification, disease evolution mechanism and disease prevention and treatment strategies are still under investigation. The development and application of artificial intelligence provides a powerful tool for the analysis of pathological myopia related data. More and more accurate data information is obtained in the clinical work and clinical research of pathological myopia through the standardized collection and acquisition of the fundus image data, the automatic segmentation and quantitative analysis of the fundus physiological structure, the automatic detection and analysis of the pathological myopia classic lesions and the clinical diagnosis and treatment decision aid, which helps ophthalmologists to understand the pathogenesis and evolution of pathological myopia.

Citation： Xu Xun, Yu Qi. Application and prospect of artificial intelligence in the analysis of fundus images of pathological myopia. Chinese Journal of Ocular Fundus Diseases, 2019, 35(5): 427-431. doi: 10.3760/cma.j.issn.1005-1015.2019.05.001 Copy

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24.	Jonas JB, Wang YX, Zhang Q, et al. Parapapillary gamma zone and axial elongation-associated optic disc rotation: the Beijing Eye Study[J]. Invest Ophthalmol Vis Sci, 2016, 57(2): 396-402. DOI: 10.1167/iovs.15-18263.
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27.	Liu X, Bi L, Xu Y. Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images[J]. Biomed Opt Express, 2019, 10(4): 1601-1612. DOI: 10.1364/BOE.10.001601.
28.	Cheng J, Liu J, Wong DWK, et al. Automatic optic disc segmentation with peripapillary atrophy elimination[J]. Conf Proc IEEE Eng Med Biol Soc, 2011, 2011: 6224-6227. DOI: 10.1109/IEMBS.2011.6091537.
29.	Xu Y, Yan K, Kim J, et al. Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy[J]. Biomed Opt Express, 2017, 8(9): 4061-4076. DOI: 10.1364/BOE.8.004061.
30.	Wolf-Dieter V, 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]. Invest Ophthalmol Vis Sci, 2017, 58(10): 4173-4181. DOI: 10.1167/iovs.17-21878.
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1. Ohno-Matsui K, Ikuno Y, Lai TYY, et al. Updates of pathologic myopia[J]. Prog Retin Eye Res, 2016, 52: 156-187. DOI: 10.1016/j.preteyeres.2015.12.001.
2. 谢红莉, 谢作揩, 叶景, 等. 我国青少年近视现患率及相关因素分析[J]. 中华医学杂志, 2010, 90(7): 439-442. DOI: 10.3760/cma.j.issn.0376-2491.2010.07.003.Xie HL, Xie ZK, Ye J, et al. Analysis of correlative factors and prevalence on China's youth myopia[J]. Natl Med J China, 2010, 90(7): 439-442. DOI: 10.3760/cma.j.issn.0376-2491.2010.07.003.
3. Cheung CMG, Arnold JJ, Holz FG, et al. Myopic choroidal neovascularization: review, guidance, and consensus statement on management[J]. Ophthalmology, 2017, 124(11): 1690-1711. DOI: 10.1016/j.ophtha.2017.04.028.
4. 徐捷, 徐亮. 近视防控的六维度评估及防控模式[J]. 中华眼视光学与视觉科学杂志, 2018, 20(3): 129-132. DOI: 10.3760/cma.j.issn.1674-845X.2018.03.001.Xu J, Xu L. Six dimensional evaluation for myopia prevention and control[J]. Chin J Optom Ophthalmol Vis Sci, 2018, 20(3): 129-132. DOI: 10.3760/cma.j.issn.1674-845X.2018.03.001.
5. Gao LQ, Liu W, Liang YB, et al. Prevalence and characteristics of myopic retinopathy in a rural Chinese adult population: the Handan Eye Study[J]. Arch Ophthalmol, 2011, 129(9): 1199-1204. DOI: 10.1001/archophthalmol.2011.230.
6. Ohno-Matsui K, Kawasaki R, Jonas JB, et al. International photographic classification and grading system for myopic maculopathy[J]. Am J Ophthalmol, 2015, 159(5): 877-883. DOI: 10.1016/j.ajo.2015.01.022.
7. Ruiz-Medrano J, Montero JA, Flores-Moreno I, et al. Myopic maculopathy: current status and proposal for a new classification and grading system (ATN)[J]. Prog Retin Eye Res, 2019, 69: 80-115. DOI: 10.1016/j.preteyeres.2018.10.005.
8. LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.
9. Long E, Lin H, Liu Z, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts[J/OL]. Nature Biomedical Engineering, 2017, 1(2): 0024[2017-01-30]. https://www.nature.com/articles/s41551-016-0024.
10. Wong TY, Bressler NM. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening[J]. JAMA, 2016, 316(22): 2366-2367. DOI: 10.1001/jama.2016.17563.
11. Lancet T. Artificial intelligence in health care: within touching distance[J/OL]. Lancet, 2018, 390(10114): 2739[2018-12-23]. https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(17)31540-4. DOI: 10.1016/S0140-6736(17)31540-4.
12. 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.
13. Poplin R, Varadarajan AV, Blumer K, et al. Predicting cardiovascular risk factors from retinal fundus photographs using deep learning[J]. Nature Biomedical Engineering, 2018, 2(3): 158-164. DOI: 10.1038/s41551-018-0195-0.
14. Kemany 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.
15. 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 Ophthalmol, 2018, 136(12): 1359-1366. DOI: 10.1001/jamaophthalmol.2018.4118.
16. Burlina PM, Joshi N, Pacheco D, et al. Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration[J]. JAMA Ophthalmol, 2017, 135(11): 1170-1176. DOI: 10.1001/jamaophthalmol.2018.4118.
17. Akram S, Javed MY, Hussain A, et al. Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques[J]. Journal of Experimental & Theoretical Artificial Intelligence, 2015, 27(6): 737-751.
18. Chen SJ, Cheng CY, Li AF, et al. Prevalence and associated risk factors of myopic maculopathy in elderly Chinese: the Shihpai eye study[J]. Invest Ophthalmol Vis Sci, 2012, 53(8): 4868-4873. DOI: 10.1167/iovs.12-9919.
19. 邓骏杰, 何鲜桂, 许迅. 高度近视巩膜厚度研究现状与进展[J]. 中华眼底病杂志, 2017, 33(1): 87-89. DOI: 10.3760/cma.j.issn.1005-1015.2017.01.028.Deng JJ, He XG, Xu X. Scleral thickness in high myopic eyes[J]. Chin J Ocul Fundus Dis, 2017, 33(1): 87-89. DOI: 10.3760/cma.j.issn.1005-1015.2017.01.028.
20. El Matri L, Bouladi M, Chebil A, et al. Choroidal thickness measurement in highly myopic eyes using SD-OCT[J]. Ophthalmic Surg Lasers Imaging, 2012, 43(6 Suppl): S38-43. DOI: 10.3928/15428877-20121001-02.
21. Xiong S, He X, Deng J, et al. Choroidal thickness in 3001 Chinese children aged 6 to 19 years using swept-source OCT[J/OL]. Sci Rep, 2017, 7: 45059[2017-03-22]. http://dx.doi.org/10.1038/srep45059. DOI: 10.1038/srep45059.
22. Hayashi K, Ohno-Matsui K, Shimada N, et al. Long-term pattern of progression of myopic maculopathy[J]. Ophthalmology, 2010, 117(8): 1595-1611. DOI: 10.1016/j.ophtha.2009.11.003.
23. Hayashi K, Ohno-Matsui K, Yoshida T, et al. Characteristics of patients with a favorable natural course of myopic choroidal neovascularization[J]. Graefe's Arch Clin Exp Ophthalmol, 2005, 243(1): 13-19. DOI: 10.1007/s00417-004-0960-5.
24. Jonas JB, Wang YX, Zhang Q, et al. Parapapillary gamma zone and axial elongation-associated optic disc rotation: the Beijing Eye Study[J]. Invest Ophthalmol Vis Sci, 2016, 57(2): 396-402. DOI: 10.1167/iovs.15-18263.
25. Battaglia PM, Iacono P, Romano F, et al. Fluorescein leakage and optical coherence tomography features of choroidal neovascularization secondary to pathologic myopia[J]. Invest Ophthalmol Vis Sci, 2018, 59(7): 3175-3180. DOI: 10.1167/iovs.17-23640.
26. Antony BJ, Abràmoff MD, Harper MM, et al. A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes[J]. Biomed Opt Express, 2013, 4(12): 2712-2728. DOI: 10.1364/BOE.4.002712.
27. Liu X, Bi L, Xu Y. Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images[J]. Biomed Opt Express, 2019, 10(4): 1601-1612. DOI: 10.1364/BOE.10.001601.
28. Cheng J, Liu J, Wong DWK, et al. Automatic optic disc segmentation with peripapillary atrophy elimination[J]. Conf Proc IEEE Eng Med Biol Soc, 2011, 2011: 6224-6227. DOI: 10.1109/IEMBS.2011.6091537.
29. Xu Y, Yan K, Kim J, et al. Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy[J]. Biomed Opt Express, 2017, 8(9): 4061-4076. DOI: 10.1364/BOE.8.004061.
30. Wolf-Dieter V, 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]. Invest Ophthalmol Vis Sci, 2017, 58(10): 4173-4181. DOI: 10.1167/iovs.17-21878.
31. 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. DOI: 10.1016/j.ophtha.2017.12.034.
32. Schmidt-Erfurth U, Sadeghipour A, Gerendas B, et al. Artificial intelligence in retina[J]. Prog Retin Eye Res, 2018, 67: 1-29. DOI: 10.1016/j.preteyeres.2018.07.004.

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Application and prospect of artificial intelligence in the analysis of fundus images of pathological myopia

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