Insights and prospectives of ophthalmologic artificial intelligence technology_Chinese Journal of Ocular Fundus Diseases

Authors：

 Chen Youxin ¹ , Zhang Bilei ¹ , Zhang Hongzhe ²

1. Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China;
2. Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China;

Corresponding author：

Chen Youxin, Email: chenyouxinpumch@163.com

Keywords：

Artificial intelligence; Diabetic retinopathy; Macular degeneration; Glaucoma; Editorial

DOI：

10.3760/cma.j.issn.1005-1015.2019.02.003

Video：

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Abstract Full text Figures/Tables Video References Cited by

For the past few years, artificial intelligence (AI) technology has developed rapidly and has become frontier and hot topics in medical research. While the deep learning algorithm based on artificial neural networks is one of the most representative tool in this field. The advancement of ophthalmology is inseparable from a variety of imaging methods, and the pronounced convenience and high efficiency endow AI technology with promising applications in screening, diagnosis and follow-up of ophthalmic diseases. At present, related research on ophthalmologic AI technology has been carried out in terms of multiple diseases and multimodality. Many valuable results have been reported aiming at several common diseases of ophthalmology. It should be emphasized that ophthalmic AI products are still faced with some problems towards practical application. The regulatory mechanism and evaluation criteria have not yet integrated as a standardized system. There are still a number of aspects to be optimized before large-scale distribution in clinical utility. Briefly, the innovation of ophthalmologic AI technology is attributed to multidisciplinary cooperation, which is of great significance to China's public health undertakings, and will be bound to benefit patients in future clinical practice.

Citation： Chen Youxin, Zhang Bilei, Zhang Hongzhe. Insights and prospectives of ophthalmologic artificial intelligence technology. Chinese Journal of Ocular Fundus Diseases, 2019, 35(2): 119-123. doi: 10.3760/cma.j.issn.1005-1015.2019.02.003 Copy

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1. Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina[J]. Prog Retin Eye Res, 2018, 67: 1-29. DOI: 10.1016/j.preteyeres.2018.07.004.
2. enkatesan R, Chandakkar P, Li B, et al. Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features[C]. International Conference of the IEEE Engineering in Medicine & Biology Society: IEEE, 2012.
3. Abràmoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning[J]. Invest Ophthalmol Vis Sci, 2016, 57(13): 5200-5206. DOI: 10.1167/iovs.16-19964.
4. Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning[J]. Ophthalmology, 2017, 124(7): 962-969. DOI: 10.1016/j.ophtha.2017.02.008.
5. Li Z, Keel S, Liu C, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs[J/OL]. Diabetes Care, 2018, 2018: 1[2018-12-01]. https://www.x-mol.com/paper/837329. DOI: 10.2337/dc18-0147.
6. van der HAA, Abramoff MD, Verbraak F, et al. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System[J]. Acta Ophthalmol, 2018, 96(1): 63-68. DOI: 10.1111/aos.13613.
7. 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, 2017, 135(11): 1170-1176. DOI: 10.1001/jamaophthalmol.2017.3782.
8. Velezmontoya R, Oliver SCN, Olson JL, et al. Current knowledge and trends in age-related macular degeneration: genetics, epidemiology, and prevention[J]. Retina, 2014, 34(3): 423-441. DOI: 10.1097/IAE.0000000000000036.
9. ElTanboly A, Ismail M, Shalaby A, et al. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images[J]. Med Phys, 2017, 44(3): 914-923. DOI: 10.1002/mp.12071.
10. Treder M, Lauermann JL, Eter N. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning[J]. Graefe’s Arch Clin Exp Ophthalmol, 2018, 256(2): 259-265. DOI: 10.1007/s00417-017-3850-3.
11. Samina K, Usman AM, Taimur H, et al. Fully automated robust system to detect retinal edema, central serous chorioretinopathy, and age related macular degeneration from optical coherence tomography images[J/OL]. Biomed Res Int, 2017, 2017: 7148245[2017-03-23]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5382397/. DOI: 10.1155/2017/7148245.
12. Sivaswamy J, Krishnadas SR, Chakravarty A, et al. A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis[DB]. JSM Biomed Imaging Data, 2015, 2(1): 1004.
13. Hiroki M, Tabuchi H, Nakakura S, et al. Deep-learning classifier with an ultra-wide-field scanning laser ophthalmoscope detects glaucoma visual field severity[J]. J Glaucoma, 2018, 27(7): 1.
14. Varadarajan AV, Ryan P, Katy B, et al. Deep learning for predicting refractive error from retinal fundus images[J]. Invest Ophthalmol Vis Sci, 2018, 59(7): 2861-2868. DOI: 10.1167/iovs.18-23887.
15. Xiao S, Bucher F, Wu Y, et al. Fully automated, deep learning segmentation of oxygen-induced retinopathy images[J/OL]. Jci Insight, 2017, 2(24): 97585[2017-12-21]. https://www.ncbi.nlm.nih.gov/pmc/?term=10.1172%2Fjci.insight.97585. DOI: 10.1172/jci.insight.97585.
16. Ohsugi H, Tabuchi H, Enno H, et al. Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment[J/OL]. Sci Rep, 2017, 7: 9425[2017-08-25]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5573327/. DOI: 10.1038/s41598-017-09891-x.
17. Damato B, Eleuteri A, Fisher AC, et al. Artificial neural networks estimating survival probability after treatment of choroidal melanoma[J]. Ophthalmology, 2008, 115(9): 1598-1607. DOI: 10.1016/j.ophtha.2008.01.032.
18. Poplin R, Varadarajan AV, Blumer K, et al. Predicting cardiovascular risk factors from retinal fundus photographs using deep learning[J/OL]. 2017, 2017: 09843[2017-08-21]. https://arxiv.org/abs/1708.09843. DOI: 10.1038/s41551-018-0195-0.
19. O’Bryhim BE, Apte RS, Kung N, et al. Association of preclinical Alzheimer disease with optical coherence tomographic angiography findings[J]. JAMA ophthalmol, 2018, 136(11): 1242-1248. DOI: 10.1001/jamaophthalmol.2018.3556.
20. Prentasic P, Loncaric S, Vatavuk Z, et al. Diabetic retinopathy image database(DRiDB): a new database for diabetic retinopathy screening programs research[C]. International Symposium on Image & Signal Processing & Analysis, 2014.

Chinese Journal of Ocular Fundus Diseases

Insights and prospectives of ophthalmologic artificial intelligence technology

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