Strengthening research on high myopia_Chinese Journal of Ocular Fundus Diseases

Authors：

 Xu Xun , Su Li

Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Eye Disease Prevention and Treatment Center, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai 200080, China;

Corresponding author：

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

Keywords：

Myopia; Myopia, degenerative; Preventive medicine; Editorial

DOI：

10.3760/cma.j.cn511434-20220422-00236

Video：

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Myopia has become a major problem that threatens human health worldwide. Complications caused by high myopia are one of the leading causes of low vision and blindness. As a chronic disease that seriously threatens ocular health in the clinical practice and public health fields, the prevention and control of high myopia should actively promote a tertiary prevention strategy, and take advantages of the latest fundus imaging technology and big data technology, artificial intelligence to explore the evolution mechanism of “myopia→high myopia→pathological myopia”. Special efforts should be focused on the establishment of a scientific myopia prediction model, implementation of effective high myopia monitoring and management, and early detection and treatment of complications of high myopia to reduce the incidence of low vision and blindness.

Citation： Xu Xun, Su Li. Strengthening research on high myopia. Chinese Journal of Ocular Fundus Diseases, 2022, 38(6): 432-435. doi: 10.3760/cma.j.cn511434-20220422-00236 Copy

1.	Fricke TR, Jong M, Naidoo KS, et al. Global prevalence of visual impairment associated with myopic macular degeneration and temporal trends from 2000 through 2050: systematic review, meta-analysis and modelling[J]. Br J Ophthalmol, 2018, 102(7): 855-862. DOI: 10.1136/bjophthalmol-2017-311266.
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8.	Ma Y, Lin S, Zhu J, et al. Different patterns of myopia prevalence and progression between internal migrant and local resident school children in Shanghai, China: a 2-year cohort study[J]. BMC Ophthalmol, 2018, 18(1): 53. DOI: 10.1186/s12886-018-0716-3.
9.	Ye B, Liu K, Cao S, et al. Discrimination of indoor versus outdoor environmental state with machine learning algorithms in myopia observational studies[J]. J Transl Med, 2019, 17(1): 314. DOI: 10.1186/s12967-019-2057-2.
10.	Du Y, Chen Q, Fan Y, et al. Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods[J]. J Transl Med, 2021, 19(1): 167. DOI: 10.1186/s12967-021-02818-1.
11.	Ye L, Li S, Shi Y, et al. Comparisons of atropine versus cyclopentolate cycloplegia in myopic children[J]. Clin Exp Optom, 2021, 104(2): 143-150. DOI: 10.1111/cxo.13128.
12.	Ye L, Shi Y, Yin Y, et al. Effects of atropine treatment on choroidal thickness in myopic children[J]. Invest Ophthalmol Vis Sci, 2020, 61(14): 15. DOI: 10.1167/iovs.61.14.15.
13.	Jiang H, Chen X, Shi F, et al. Improved cGAN based linear lesion segmentation in high myopia ICGA images[J]. Biomed Opt Express, 2019, 10(5): 2355-2366. DOI: 10.1364/BOE.10.002355.
14.	Luo G, Chen X, Shi F, et al. Multimodal affine registration for ICGA and MCSL fundus images of high myopia[J]. Biomed Opt Express, 2020, 11(8): 4443-4457. DOI: 10.1364/BOE.393178.
15.	Xie J, Chen Q, Yu J, et al. Morphologic features of myopic choroidal neovascularization in pathologic myopia on swept-source optical coherence tomography[J/OL]. Front Med (Lausanne), 2020, 7: 615902[2020-12-23]. https://pubmed.ncbi.nlm.nih.gov/33425961/. DOI: 10.3389/fmed.2020.615902.
16.	Chen Q, He J, Yin Y, et al. Impact of the morphologic characteristics of optic disc on choroidal thickness in young myopic patients[J]. Invest Ophthalmol Vis Sci, 2019, 60(8): 2958-2967. DOI: 10.1167/iovs.18-26393.
17.	Deng J, Jin J, Lv M, et al. Distribution of scleral thickness and associated factors in 810 Chinese children and adolescents: a swept-source optical coherence tomography study[J/OL]. Acta Ophthalmol, 2019, 97(3): e410-e418[2018-09-04]. https://pubmed.ncbi.nlm.nih.gov/30178606/. DOI: 10.1111/aos.13788.
18.	Cheng L, Wang M, Deng J, et al. Macular ganglion cell-inner plexiform layer, ganglion cell complex, and outer retinal layer thicknesses in a large cohort of Chinese children[J]. Invest Ophthalmol Vis Sci, 2019, 60(14): 4792-4802. DOI: 10.1167/iovs.18-26300.
19.	He XG, Deng JJ, Yin Y, et al. Macular choroidal thickness in Chinese preschool children: decrease with axial length but no evident change with age[J]. Int J Ophthalmol, 2019, 12(9): 1465-1473. DOI: 10.18240/ijo.2019.09.15.
20.	Jin P, Zou H, Xu X, et al. Longitudinal changes in choroidal and retinal thicknesses in children with myopic shift[J]. Retina, 2019, 39(6): 1091-1099. DOI: 10.1097/IAE.0000000000002090.
21.	He J, Chen Q, Yin Y, et al. Association between retinal microvasculature and optic disc alterations in high myopia[J]. Eye (Lond), 2019, 33(9): 1494-1503. DOI: 10.1038/s41433-019-0438-7.
22.	Su L, Ji YS, Tong N, et al. Quantitative assessment of the retinal microvasculature and choriocapillaris in myopic patients using swept-source optical coherence tomography angiography[J]. Graefe's Arch Clin Exp Ophthalmol, 2020, 258(6): 1173-1180. DOI: 10.1007/s00417-020-04639-2.
23.	Ting DSW, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations[J/OL]. Prog Retin Eye Res, 2019, 72: 100759[2019-04-29]. https://pubmed.ncbi.nlm.nih.gov/31048019/. DOI: 10.1016/j.preteyeres.2019.04.003.
24.	Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology[J]. Br J Ophthalmol., 2019, 103(2): 167-175. DOI: 10.1136/bjophthalmol-2018-313173.
25.	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.
26.	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.
27.	Chen Q, He J, Hu G, et al. Morphological characteristics and risk factors of myopic maculopathy in an older high myopia population-based on the new classification system (ATN)[J]. Am J Ophthalmol, 2019, 208: 356-366. DOI: 10.1016/j.ajo.2019.07.010.

1. Fricke TR, Jong M, Naidoo KS, et al. Global prevalence of visual impairment associated with myopic macular degeneration and temporal trends from 2000 through 2050: systematic review, meta-analysis and modelling[J]. Br J Ophthalmol, 2018, 102(7): 855-862. DOI: 10.1136/bjophthalmol-2017-311266.
2. Ye L, Chen Q, Hu G, et al. Distribution and association of visual impairment with myopic maculopathy across age groups among highly myopic eyes-based on the new classification system (ATN)[J/OL]. Acta Ophthalmol, 2022, 100(4): e957-e967[2021-10-04]. https://pubmed.ncbi.nlm.nih.gov/34605605/. DOI: 10.1111/aos.15020.
3. Xiong S, Sankaridurg P, Naduvilath T, et al. Time spent in outdoor activities in relation to myopia prevention and control: a meta-analysis and systematic review[J]. Acta Ophthalmol, 2017, 95(6): 551-566. DOI: 10.1111/aos.13403.
4. He X, Sankaridurg P, Xiong S, et al. Shanghai time outside to reduce myopia trial: design and baseline data[J]. Clin Exp Ophthalmol, 2019, 47(2): 171-178. DOI: 10.1111/ceo.13391.
5. Wang J, He XG, Xu X. The measurement of time spent outdoors in child myopia research: a systematic review[J]. Int J Ophthalmol, 2018, 11(6): 1045-1052. DOI: 10.18240/ijo.2018.06.24.
6. Liu XN, Naduvilath TJ, Wang J, et al. Sleeping late is a risk factor for myopia development amongst school-aged children in China[J/OL]. Sci Rep, 2020, 10(1): 17194[2021-02-23]. https://pubmed.ncbi.nlm.nih.gov/33057123/. DOI: 10.1038/s41598-020-74348-7.
7. He X, Zhao R, Sankaridurg P, et al. Design and methodology of the Shanghai child and adolescent large-scale eye study (SCALE)[J]. Clin Exp Ophthalmol, 2018, 46(4): 329-338. DOI: 10.1111/ceo.13065.
8. Ma Y, Lin S, Zhu J, et al. Different patterns of myopia prevalence and progression between internal migrant and local resident school children in Shanghai, China: a 2-year cohort study[J]. BMC Ophthalmol, 2018, 18(1): 53. DOI: 10.1186/s12886-018-0716-3.
9. Ye B, Liu K, Cao S, et al. Discrimination of indoor versus outdoor environmental state with machine learning algorithms in myopia observational studies[J]. J Transl Med, 2019, 17(1): 314. DOI: 10.1186/s12967-019-2057-2.
10. Du Y, Chen Q, Fan Y, et al. Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods[J]. J Transl Med, 2021, 19(1): 167. DOI: 10.1186/s12967-021-02818-1.
11. Ye L, Li S, Shi Y, et al. Comparisons of atropine versus cyclopentolate cycloplegia in myopic children[J]. Clin Exp Optom, 2021, 104(2): 143-150. DOI: 10.1111/cxo.13128.
12. Ye L, Shi Y, Yin Y, et al. Effects of atropine treatment on choroidal thickness in myopic children[J]. Invest Ophthalmol Vis Sci, 2020, 61(14): 15. DOI: 10.1167/iovs.61.14.15.
13. Jiang H, Chen X, Shi F, et al. Improved cGAN based linear lesion segmentation in high myopia ICGA images[J]. Biomed Opt Express, 2019, 10(5): 2355-2366. DOI: 10.1364/BOE.10.002355.
14. Luo G, Chen X, Shi F, et al. Multimodal affine registration for ICGA and MCSL fundus images of high myopia[J]. Biomed Opt Express, 2020, 11(8): 4443-4457. DOI: 10.1364/BOE.393178.
15. Xie J, Chen Q, Yu J, et al. Morphologic features of myopic choroidal neovascularization in pathologic myopia on swept-source optical coherence tomography[J/OL]. Front Med (Lausanne), 2020, 7: 615902[2020-12-23]. https://pubmed.ncbi.nlm.nih.gov/33425961/. DOI: 10.3389/fmed.2020.615902.
16. Chen Q, He J, Yin Y, et al. Impact of the morphologic characteristics of optic disc on choroidal thickness in young myopic patients[J]. Invest Ophthalmol Vis Sci, 2019, 60(8): 2958-2967. DOI: 10.1167/iovs.18-26393.
17. Deng J, Jin J, Lv M, et al. Distribution of scleral thickness and associated factors in 810 Chinese children and adolescents: a swept-source optical coherence tomography study[J/OL]. Acta Ophthalmol, 2019, 97(3): e410-e418[2018-09-04]. https://pubmed.ncbi.nlm.nih.gov/30178606/. DOI: 10.1111/aos.13788.
18. Cheng L, Wang M, Deng J, et al. Macular ganglion cell-inner plexiform layer, ganglion cell complex, and outer retinal layer thicknesses in a large cohort of Chinese children[J]. Invest Ophthalmol Vis Sci, 2019, 60(14): 4792-4802. DOI: 10.1167/iovs.18-26300.
19. He XG, Deng JJ, Yin Y, et al. Macular choroidal thickness in Chinese preschool children: decrease with axial length but no evident change with age[J]. Int J Ophthalmol, 2019, 12(9): 1465-1473. DOI: 10.18240/ijo.2019.09.15.
20. Jin P, Zou H, Xu X, et al. Longitudinal changes in choroidal and retinal thicknesses in children with myopic shift[J]. Retina, 2019, 39(6): 1091-1099. DOI: 10.1097/IAE.0000000000002090.
21. He J, Chen Q, Yin Y, et al. Association between retinal microvasculature and optic disc alterations in high myopia[J]. Eye (Lond), 2019, 33(9): 1494-1503. DOI: 10.1038/s41433-019-0438-7.
22. Su L, Ji YS, Tong N, et al. Quantitative assessment of the retinal microvasculature and choriocapillaris in myopic patients using swept-source optical coherence tomography angiography[J]. Graefe's Arch Clin Exp Ophthalmol, 2020, 258(6): 1173-1180. DOI: 10.1007/s00417-020-04639-2.
23. Ting DSW, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations[J/OL]. Prog Retin Eye Res, 2019, 72: 100759[2019-04-29]. https://pubmed.ncbi.nlm.nih.gov/31048019/. DOI: 10.1016/j.preteyeres.2019.04.003.
24. Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology[J]. Br J Ophthalmol., 2019, 103(2): 167-175. DOI: 10.1136/bjophthalmol-2018-313173.
25. 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.
26. 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.
27. Chen Q, He J, Hu G, et al. Morphological characteristics and risk factors of myopic maculopathy in an older high myopia population-based on the new classification system (ATN)[J]. Am J Ophthalmol, 2019, 208: 356-366. DOI: 10.1016/j.ajo.2019.07.010.

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