Understanding the development status of ophthalmic artificial intelligence, holding the challenges and opportunities_Chinese Journal of Ocular Fundus Diseases

Authors：

 Zhang Ming , Zhou Sirui

Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu 610041, China;
Zhou Sirui now is working at Department of Ophthalmology, The Third People's Hospital of Chengdu, Chengdu 610031, China;

Corresponding author：

Zhang Ming, Email: zhangmingscu@126.com

Keywords：

Artificial intelligence; Diagnostic techniques, ophthalmological; Editorial

DOI：

10.3760/cma.j.cn511434-20210121-00044

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the rapid development of artificial intelligence (AI), especially deep learning, AI research in the field of ophthalmology has presented a trend of diversification in disease types, generalization in scenarios and deepening in researches. The AI algorithm has showed a good performance in the studies of diabetic retinopathy, age-related macular degeneration, glaucoma and other ocular diseases, yielding up the great potential of ophthalmic AI. However, most studies are still in their infancy, and the application of ophthalmic AI still faces many challenges such as lack of interpretability for results, deficiency of data standardization, and insufficiency of clinical applicability. At the same time, it should also be noted that the development of multi-modal imaging, the innovation of digital technologies (such as 5G and the Internet of Things) and telemedicine, and the new discovery that retina status can reflect systemic diseases have brought new opportunities for the development of ophthalmic AI. Learn the current status of AI research in the field of ophthalmology, grasp the new challenges and opportunities in its development process, successfully realizing the transformation of ophthalmic AI from research to practical application.

Citation： Zhang Ming, Zhou Sirui. Understanding the development status of ophthalmic artificial intelligence, holding the challenges and opportunities. Chinese Journal of Ocular Fundus Diseases, 2021, 37(2): 93-97. doi: 10.3760/cma.j.cn511434-20210121-00044 Copy

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1. LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.
2. 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://linkinghub.elsevier.com/retrieve/pii/S1350-9462(18)30090-9. DOI: 10.1016/j.preteyeres.2019.04.003.
3. Saba L, Biswas M, Kuppili V, et al. The present and future of deep learning in radiology[J]. Eur J Radiol, 2019, 114: 14-24. DOI: 10.1016/j.ejrad.2019.02.038.
4. Dey D, Slomka PJ, Leeson P, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review[J]. J Am Coll Cardiol, 2019, 73(11): 1317-1335. DOI: 10.1016/j.jacc.2018.12.054.
5. Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence[J]. Lancet Oncol, 2019, 20(5): e253-e261. DOI: 10.1016/s1470-2045(19)30154-8.
6. 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.
7. Abramoff 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.
8. Ting DS, Cheung GC, Wong TY. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review[J]. Clin Exp Ophthalmol, 2016, 44(4): 260-277. DOI: 10.1111/ceo.12696.
9. Aguiree F, Brown A, Cho NH, et al. IDF diabetes atlas: sixth edition[EB/OL]. [2013-05-27]. http://www.doc88.com/p-2357735573023.html.
10. Wang S, Zhang Y, Lei S, et al. Performance of deep neural network-based artificial intelligence method in diabetic retinopathy screening: a systematic review and meta-analysis of diagnostic test accuracy[J]. Eur J Endocrinol, 2020, 183(1): 41-49. DOI: 10.1530/eje-19-0968.
11. 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]. Diabetes Care, 2018, 41(12): 2509-2516. DOI: 10.2337/dc18-0147.
12. Sayres R, Taly A, Rahimy E, et al. Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy[J]. Ophthalmology, 2019, 126(4): 552-564. DOI: 10.1016/j.ophtha.2018.11.016.
13. Bellemo V, Lim ZW, Lim G, et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study[J]. Lancet Digit Health, 2019, 1(1): e35-e44. DOI: 10.1016/s2589-7500(19)30004-4.
14. Gulshan V, Rajan RP, Widner K, et al. Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India[J]. JAMA Ophthalmol, 2019, 137(9): 987-993. DOI: 10.1001/jamaophthalmol.2019.2004.
15. Son J, Shin JY, Kim HD, et al. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images[J]. Ophthalmology, 2020, 127(1): 85-94. DOI: 10.1016/j.ophtha.2019.05.029.
16. Ting DSW, Cheung YL, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes[J]. JAMA, 2017, 318(22): 2211-2223. DOI: 10.1001/jama.2017.18152.
17. Xie Y, Nguyen QD, Hamzah H, et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national program: a modelled economic analysis study[J/OL]. Lancet Digit Health, 2020, 2(5): E240-E249[2020-05-01]. https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30060-1/fulltext. DOI: 10.2139/ssrn.3498440.
18. Treder M, Diener R, Eter N. Artificial intelligence in management of macular edema: Opportunities and challenges[J]. Ophthalmologe, 2020, 117(10): 989-992. DOI: 10.1007/s00347-020-01110-9.
19. Xie Q, Liu Y, Huang H, et al. An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms[J/OL]. Pharmacol Res, 2020, 159: 104986[2020-06-02]. https://linkinghub.elsevier.com/retrieve/pii/S1043-6618(20)31294-9. DOI: 10.1016/j.phrs.2020.104986.
20. Al-Zamil WM, Yassin SA. Recent developments in age-related macular degeneration: a review[J]. Clin Interv Aging, 2017, 12: 1313-1330. DOI: 10.2147/CIA.S143508.
21. Wong WL, Su X, Li X, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis[J]. Lancet Glob Health, 2014, 2(2): e106-116. DOI: 10.1016/s2214-109x(13)70145-1.
22. Flaxel CJ, Adelman RA, Bailey ST, et al. Age-related macular degeneration preferred practice pattern®[J]. Ophthalmology, 2020, 127(1): P1-P65. DOI: 10.1016/j.ophtha.2019.09.024.
23. 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.
24. 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. DOI: 10.1016/j.cell.2018.02.010.
25. Venhuizen FG, van Ginneken B, van Asten F, et al. Automated staging of age-related macular degeneration using optical coherence tomography[J]. Invest Opthalmol Vis Sci, 2017, 58(4): 2318-2328. DOI: 10.1167/iovs.16-20541.
26. Grassmann F, Mengelkamp J, Brandl C, et al. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography[J]. Ophthalmology, 2018, 125(9): 1410-1420. DOI: 10.1016/j.ophtha.2018.02.037.
27. Chakravarthy U, Goldenberg D, Young G, et al. Automated identification of lesion activity in neovascular age-related macular degeneration[J]. Ophthalmology, 2016, 123(8): 1731-1736. DOI: 10.1016/j.ophtha.2016.04.005.
28. Prahs P, Radeck V, Mayer C, et al. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications[J]. Graefe’s Arch Clin Exp Ophthalmol, 2018, 256(1): 91-98. DOI: 10.1007/s00417-017-3839-y.
29. Bogunovic H, Montuoro A, Baratsits M, et al. Machine learning of the progression of intermediate age-related macular degeneration based on OCT imaging[J]. Invest Ophthalmol Vis Sci, 2017, 58(6): BIO141-BIO150. DOI: 10.1167/iovs.17-21789.
30. Schmidt-Erfurth U, Waldstein SM, Klimscha S, et al. Prediction of individual disease conversion in early AMD using artificial intelligence[J]. Invest Ophthalmol Vis Sci, 2018, 59(8): 3199-3208. DOI: 10.1167/iovs.18-24106.
31. Bogunovic H, Waldstein SM, Schlegl T, et al. Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach[J]. Invest Ophthalmol Vis Sci, 2017, 58(7): 3240-3248. DOI: 10.1167/iovs.16-21053.
32. Aslam TM, Zaki HR, Mahmood S, et al. Use of a neural net to model the impact of optical coherence tomography abnormalities on vision in age-related macular degeneration[J]. Am J Ophthalmol, 2018, 185: 94-100. DOI: 10.1016/j.ajo.2017.10.015.
33. Tham YC, Li X, Wong TY, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis[J]. Ophthalmology, 2014, 121(11): 2081-2090. DOI: 10.1016/j.ophtha.2014.05.013.
34. Li F, Wang Z, Qu G, et al. Automatic differentiation of glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network[J/OL]. BMC Med Imaging, 2018, 18(1): 35[2018-10-04]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-018-0273-5. DOI: 10.1186/s12880-018-0273-5.
35. Kucur ŞS, Holló G, Sznitman R, et al. A deep learning approach to automatic detection of early glaucoma from visual fields[J/OL]. PLoS One, 2018, 13(11): e0206081[2018-11-28]. http://europepmc.org/article/MED/30485270. DOI: 10.1371/journal.pone.0206081.
36. Li Z, He Y, Keel S, et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs[J]. Ophthalmology, 2018, 125(8): 1199-1206. DOI: 10.1016/j.ophtha.2018.01.023.
37. Barella KA, Costa VP, Gonçalves Vidotti V, et al. Glaucoma diagnostic accuracy of machine learning classifiers using retinal nerve fiber layer and optic nerve data from SD-OCT[J/OL]. J Ophthalmol, 2013, 2013: 789129[2013-11-28]. http://europepmc.org/article/MED/24369495. DOI: 10.1155/2013/789129.
38. Kim SJ, Cho KJ, Oh S. Development of machine learning models for diagnosis of glaucoma[J/OL]. PLoS One, 2017, 12(5): e0177726[2017-05-23]. http://europepmc.org/article/MED/28542342. DOI: 10.1371/journal.pone.0177726.
39. Guangzhou A, Kazuko O, Satoru T, et al. Comparison of machine-learning classification models for glaucoma management[J/OL]. J Healthc Eng, 2018, 2018: 6874765[2018-06-19]. http://europepmc.org/article/MED/30018755. DOI: 10.1155/2018/6874765.
40. Christopher M, Belghith A, Weinreb RN, et al. Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict glaucoma progression[J]. Invest Ophthalmol Vis Sci, 2018, 59(7): 2748-2756. DOI: 10.1167/iovs.17-23387.
41. Yousefi S, Kiwaki T, Zheng Y, et al. Detection of longitudinal visual field progression in glaucoma using machine learning[J]. Am J Ophthalmol, 2018, 193: 71-79. DOI: 10.1016/j.ajo.2018.06.007.
42. Wen JC, Lee CS, Keane PA, et al. Forecasting future Humphrey Visual Fields using deep learning[J/OL]. PLoS One, 2019, 14(4): e0214875[2019-04-05]. http://europepmc.org/article/MED/30951547. DOI: 10.1371/journal.pone.0214875.
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