Application progress of artificial intelligence in the screening of diabetic retinopathy_Chinese Journal of Ocular Fundus Diseases

Authors：

Wu Yanrong , Xia Guimei , Gao Qingyue , Yuan Ziyou ,  Hao Shaofeng

Department of Ophtalmology, Heji Hospital of Changzhi Medical College, Changzhi 046000, China;

Corresponding author：

Hao Shaofeng, Email: 120600540@qq.com

Keywords：

Artificial intelligence; Diabetic retinopathy; Review

DOI：

10.3760/cma.j.cn511434-20200808-00384

Video：

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Artificial intelligence (AI) is an emerging science and technology that studies and develops theories, methods, technologies, and application systems for simulating and expanding human intelligence. AI has made great breakthroughs in the field of intelligent medicine, and has shown great potential in the diagnosis and treatment of diabetic retinopathy (DR), retinopathy of prematurity, and other fundus diseases. A number of clinical trials on the application of AI technologies to DR screening have been carried out in the domestic and overseas, which not only have a high accuracy rate, but also save doctors' reading time and reduce the burden of society, medical work and patients. However, due to the lack of evaluation system for DR intelligent diagnosis technology, the accuracy of AI system still lacks of big data verification. Secondly, most of the color fundus photographs are taken in the posterior 45°, which only show the most vulnerable areas, making some lesions undetectable. In addition, the current DR screening system has not yet been applied to the clinic, most of which are in the stage of prospective research and trials. There are still many obstacles from the environment to the hospital or the clinic. Doctors cannot use real patient data to evaluate the AI system, so it is not popular in clinical practice. In the future, DR screening algorithms and diagnostic models can be further improved and established to make DR AI screening more accurate.

Citation： Wu Yanrong, Xia Guimei, Gao Qingyue, Yuan Ziyou, Hao Shaofeng. Application progress of artificial intelligence in the screening of diabetic retinopathy. Chinese Journal of Ocular Fundus Diseases, 2021, 37(6): 491-494. doi: 10.3760/cma.j.cn511434-20200808-00384 Copy

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9.	Shah P, Mishra DK, Shanmugam MP, et al. Validation of deep convolutional neural network-based algorithm for detection of diabetic retinopathy-artificial intelligence versus clinician for screening[J]. Indian J Ophthalmol, 2020, 68(2): 398-405. DOI: 10.4103/ijo.IJO_966_19.
10.	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: 10.22608/APO.201976.
11.	Abràmoff MD, Folk JC, Han DP, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy[J]. JAMA Ophthalmol, 2013, 131(3): 351-357. DOI: 10.1001/jamaophthalmol.2013.1743.
12.	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.
13.	Solanki K, Ramachandra C, Bhat S, et al. EyeArt: automated, high-throughput, image analysis for diabetic retinopathy screening[J]. Invest Ophthalmol Vis Sci, 2015, 56(7): 1429.
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20.	高韶晖, 金学民, 赵朝霞, 等. 糖尿病视网膜病变人工智能机器人辅助诊断系统的建立及应用[J]. 中华实验眼科杂志, 2019, 37(8): 669-673. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.016.Gao SH, Jin XM, Zhao CX, et al. Validation and application of an artificial intelligence robot assisted diagnosis system for diabetic retinopathy[J]. Chin J Exp Ophthalmol, 2019, 37(8): 669-673. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.016.
21.	李萌, 王耿媛, 夏鸿慧, 等. 眼底阅片人工智能系统在糖尿病视网膜病变筛查中的临床价值评价[J]. 中华实验眼科杂志, 2019, 37(8): 663-668. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.015.Li M, Wang GY, Xia HH, et al. Clinical evaluation of artificial intelligence system based on fundus photograph in diabetic retinopathy screening[J]. Chin J Exp Ophthalmol, 2019, 37(8): 663-668. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.015.
22.	Sosale B, Sosale AR, Murthy H, et al. Medios-an offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy[J]. Indian J Ophthalmol, 2020, 68(2): 391-395. DOI: 10.4103/ijo.IJO_1203_19.
23.	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 Ophthalmol, 2019, 137(10): 1182-1188. DOI: 10.1001/jamaophthalmol.2019.2923.
24.	Takahashi H, Tampo H, Arai Y, et al. Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy[J/OL]. PLoS One, 2017, 12(6): e0179790[2017-06-22]. https://pubmed.ncbi.nlm.nih.gov/28640840/. DOI: 10.1371/journal.pone.0179790.
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1. 张远望. 人工智能与应用[J]. 中国科技纵览, 2015, 10(20): 22. DOI: 10.3969/j.issn.1671-2064.2015.20.020.Zhang YW. Artificial intelligence and application[J]. China Science & Technology Overview, 2015, 10(20): 22. DOI: 10.3969/j.issn.1671-2064.2015.20.020.
2. 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.
3. 朱谷雨, 张景尚, 万修华. 人工智能在眼科的应用进展[J]. 国际眼科纵览, 2019, 43(1): 14-18. DOI: 10.3760/cma.j.issn.1673-5803.2019.01.004.Zhu GY, Zhang JS, Wan XH, et al. Artificial intelligence in ophthalmology[J]. Int Rev Ophthalmol, 2019, 43(1): 14-18. DOI: 10.3760/cma.j.issn.1673-5803.2019.01.004.
4. 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/OL]. J Glob Health, 2018, 8(1): 010803[2018-08-09]. https://pubmed.ncbi.nlm.nih.gov/29899983/. DOI: 10.7189/jogh.08.010803.
5. Wild S, Roglic G, Green A, et al. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030[J]. Diabetes Care, 2004, 27(5): 1047-1053. DOI: 10.2337/diacare.27.5.1047.
6. Wong TY, Sabanayagam C. The war on diabetic retinopathy: where are we now?[J]. Asia Pac J Ophthalmol (Phila), 2019, 8(6): 448-456. DOI: 10.1097/APO.0000000000000267.
7. Yau JW, Rogers SL, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy[J]. Diabetes Care, 2012, 35(3): 556-564. DOI: 10.2337/dc11-1909.
8. He J, Cao T, Xu F, et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital[J]. Eye (Lond), 2020, 34(3): 572-576. DOI: 10.1038/s41433-019-0562-4.
9. Shah P, Mishra DK, Shanmugam MP, et al. Validation of deep convolutional neural network-based algorithm for detection of diabetic retinopathy-artificial intelligence versus clinician for screening[J]. Indian J Ophthalmol, 2020, 68(2): 398-405. DOI: 10.4103/ijo.IJO_966_19.
10. 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: 10.22608/APO.201976.
11. Abràmoff MD, Folk JC, Han DP, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy[J]. JAMA Ophthalmol, 2013, 131(3): 351-357. DOI: 10.1001/jamaophthalmol.2013.1743.
12. 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.
13. Solanki K, Ramachandra C, Bhat S, et al. EyeArt: automated, high-throughput, image analysis for diabetic retinopathy screening[J]. Invest Ophthalmol Vis Sci, 2015, 56(7): 1429.
14. Walton OB 4th, Garoon RB, Weng CY, et al. Evaluation of automated teleretinal screening program for diabetic retinopathy[J]. JAMA Ophthalmol, 2016, 134(2): 204-209. DOI: 10.1001/jamaophthalmol.2015.5083.
15. Ting DSW, Cheung CY, 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.
16. 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.
17. Ramachandran N, Hong SC, Sime M J, et al. Diabetic retinopathy screening using deep neural network[J]. Clin Exp Ophthalmol, 2018, 46(4): 412-416. DOI: 10.1111/ceo.13056.
18. 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.
19. Yang WH, Zheng B, Wu MN, et al. An evaluation system of fundus photograph-based intelligent diagnostic technology for diabetic retinopathy and applicability for research[J]. Diabetes Ther, 2019, 10(5): 1811-1822. DOI: 10.1007/s13300-019-0652-0.
20. 高韶晖, 金学民, 赵朝霞, 等. 糖尿病视网膜病变人工智能机器人辅助诊断系统的建立及应用[J]. 中华实验眼科杂志, 2019, 37(8): 669-673. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.016.Gao SH, Jin XM, Zhao CX, et al. Validation and application of an artificial intelligence robot assisted diagnosis system for diabetic retinopathy[J]. Chin J Exp Ophthalmol, 2019, 37(8): 669-673. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.016.
21. 李萌, 王耿媛, 夏鸿慧, 等. 眼底阅片人工智能系统在糖尿病视网膜病变筛查中的临床价值评价[J]. 中华实验眼科杂志, 2019, 37(8): 663-668. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.015.Li M, Wang GY, Xia HH, et al. Clinical evaluation of artificial intelligence system based on fundus photograph in diabetic retinopathy screening[J]. Chin J Exp Ophthalmol, 2019, 37(8): 663-668. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.015.
22. Sosale B, Sosale AR, Murthy H, et al. Medios-an offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy[J]. Indian J Ophthalmol, 2020, 68(2): 391-395. DOI: 10.4103/ijo.IJO_1203_19.
23. 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 Ophthalmol, 2019, 137(10): 1182-1188. DOI: 10.1001/jamaophthalmol.2019.2923.
24. Takahashi H, Tampo H, Arai Y, et al. Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy[J/OL]. PLoS One, 2017, 12(6): e0179790[2017-06-22]. https://pubmed.ncbi.nlm.nih.gov/28640840/. DOI: 10.1371/journal.pone.0179790.
25. Beede E, Baylor E, Hersch F, et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy[C/OL]. Association for Computing Machinery. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, 2020[2020-08-20]. https://dl.acm.org/doi/fullHtml/10.1145/3313831.3376718.

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