Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (11): 39-44.doi: 10.6040/j.issn.1671-7554.0.2020.1256

• Special topic on new progress in ophthalmic artificial intelligence • Previous Articles     Next Articles

Research progress of artificial intelligence diagnosis system in retinal diseases

Yi QU*(),Huankai ZHANG,Xian SONG,Baorui CHU   

  1. Department of Geriatrics, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
  • Received:2020-09-07 Online:2020-11-10 Published:2020-11-04
  • Contact: Yi QU E-mail:yiqucn@sdu.edu.cn

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Abstract:

Retinal diseases are the main cause of severe vision loss in humans. Early screening and timely follow-up of diseases such as diabetic retinopathy (DR), glaucomatous retinopathy and optic neuropathy, age-related macular degeneration (AMD) are the key of disease prevention and treatment. However, due to the limited number of ophthalmologists and the high cost of manual screening, it is difficult to carry out large-scale disease screening. The application of artificial intelligence (AI) in ophthalmology has brought dawn to solve the problems. This article reviews the application of AI in the diagnosis of DR, glaucomatous retinopathy and optic neuropathy, AMD and other diseases, discusses the application of AI diagnosis system in the teaching of retinal diseases, points out the current problems of AI systems, and looks forward to its development prospects in the field of retinopathy.

Key words: Artificial intelligence, Retinal diseases, Deep learning

CLC Number: 

  • 2020-09-07
1 Lee R , Wong TY , Sabanayagam C . Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss[J]. Eye Vis (Lond), 2015, 2: 17.
doi: 10.1186/s40662-015-0026-2
2 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
3 Wong WL , Su XY , 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- e116.
doi: 10.1016/S2214-109X(13)70145-1
4 国家卫生和计划生育委员会. "十三五"全国眼健康规划(2016—2020年)[J]. 中华眼科杂志, 2017, 53 (7): 484- 486.
5 Anand S , Fan VY , Zhang JH , et al. China's human resources for health: quantity, quality, and distribution[J]. Lancet, 2008, 372 (9651): 1774- 1781.
doi: 10.1016/S0140-6736(08)61363-X
6 McCarthy J , Minsky ML , Rochester N , et al. A proposal for the Dartmouth summer research project on artificial intelligence[J]. AI Magazine, 2006, 27 (4): 12- 14.
7 Lip GY , Nieuwlaat R , Pisters R , et al. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation[J]. Chest, 2010, 137 (2): 263- 272.
doi: 10.1378/chest.09-1584
8 LeCun Y , Bengio Y , Hinton G . Deep learning[J]. Nature, 2015, 521 (7553): 436- 444.
doi: 10.1038/nature14539
9 Russakovsky O , Deng J , Su H , et al. ImageNet large scale visual recognition challenge[J]. Int J Comput Vis, 2015, 115 (3): 211- 252.
doi: 10.1007/s11263-015-0816-y
10 Hirschberg J , Manning CD . Advances in natural language processing[J]. Science, 2015, 349 (6245): 261- 266.
doi: 10.1126/science.aaa8685
11 Garcia GP , Lavieri MS , Andrews C , et al. Accuracy of Kalman filtering in forecasting visual field and intraocular pressure trajectory in patients with ocular hypertension[J]. JAMA Ophthalmol, 2019, 137 (12)
doi: 10.1001/jamaophthalmol.2019.4190
12 Li ZX , He YF , 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
13 Yim J , Chopra R , Spitz T , et al. Predicting conversion to wet age-related macular degeneration using deep learning[J]. Nat Med, 2020, 26 (6): 892- 899.
doi: 10.1038/s41591-020-0867-7
14 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
15 Chakrabarti R , Harper CA , Keeffe JE . Diabetic retinopathy management guidelines[J]. Expert Review of Ophthalmology, 2012, 7 (5): 417- 439.
doi: 10.1586/eop.12.52
16 Solomon SD , Chew E , Duh EJ , et al. Diabetic retinopathy: a position statement by the American diabetes association[J]. Diabetes Care, 2017, 40 (3): 412- 418.
17 Ferris FL . How effective are treatments for diabetic retinopathy?[J]. JAMA, 1993, 269 (10): 1290.
doi: 10.1001/jama.1993.03500100088034
18 Liew G , Michaelides M , Bunce C . A comparison of the causes of blindness certifications in England and Wales in working age adults (16-64 years), 1999-2000 with 2009-2010[J]. BMJ Open, 2014, 4 (2): e004015.
doi: 10.1136/bmjopen-2013-004015
19 Thomas RL , Halim S , Gurudas S , et al. IDF Diabetes Atlas: a review of studies utlising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018[J]. Diabetes Res Pract, 2019, 157: 107840.
doi: 10.1016/j.diabres.2019.107840
20 Williams R , Karuranga S , Malanda B , et al. Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas, 9th edition[J]. Diabetes Res Clin Pract, 2020, 162: 108072.
doi: 10.1016/j.diabres.2020.108072
21 van der Heijden AA , 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
22 Tufail A , Kapetanakis VV , Salas-Vega S , et al. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness[J]. Health Technol Assess, 2016, 20 (92): 1- 72.
doi: 10.3310/hta20920
23 Rajalakshmi R , Subashini R , Anjana RM , et al. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence[J]. Eye (Lond), 2018, 32 (6): 1138- 1144.
doi: 10.1038/s41433-018-0064-9
24 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
25 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 Ophthalmology, 2019, 137 (9): 987- 993.
doi: 10.1001/jamaophthalmol.2019.2004
26 Abràmoff MD , Lavin PT , Birch M , et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices[J]. NPJ Digit Med, 2018, 1: 39.
doi: 10.1038/s41746-018-0040-6
27 Verbraak FD , Abramoff MD , Bausch GCF , et al. Diagnostic accuracy of a device for the automated detection of diabetic retinopathy in a primary care setting[J]. Diabetes Care, 2019, 42 (4): 651- 656.
28 Ribeiro ML , Nunes SG , Cunha-Vaz JG . Microaneurysm turnover at the macula predicts risk of development of clinically significant macular edema in persons with mild nonproliferative diabetic retinopathy[J]. Diabetes Care, 2013, 36 (5): 1254- 1259.
doi: 10.2337/dc12-1491
29 Liu S, Gong L, Ma K, et al. GREEN: a graph REsidual rE-ranking network for grading diabetic retinopathy[J]. arXiv prepeint arXiv: 2007.09968v2, 2020.
30 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
31 Resnikoff S , Pascolini D , Etya'Ale D , et al. Global data on visual impairment in the year 2002[J]. Bull World Health Organ, 2004, 82 (11): 844- 851.
32 Glaucoma Research Foundation. Five common glaucoma tests[EB/OL]. (2020-01-09)[2020-09-07]. https://www.glaucoma.org/glaucoma/diagnostic-tests.php.
33 Tatham AJ , Weinreb RN , Medeiros FA . Strategies for improving early detection of glaucoma: the combined structure-function index[J]. Clin Ophthalmol, 2014, 8: 611- 621.
doi: 10.2147/opth.s44586
34 Hennis A , Wu SY , Nemesure B , et al. Awareness of incident open-angle glaucoma in a population study: the Barbados Eye Studies[J]. Ophthalmology, 2007, 114 (10): 1816- 1821.
doi: 10.1016/j.ophtha.2007.06.013
35 Iwase A , Suzuki Y , Araie M , et al. The prevalence of primary open-angle glaucoma in Japanese: the Tajimi Study[J]. Ophthalmology, 2004, 111 (9): 1641- 1648.
36 Osborne NN , Wood JP , Chidlow G , et al. Ganglion cell death in glaucoma: what do we really know?[J]. Br J Ophthalmol, 1999, 83 (8): 980- 986.
doi: 10.1136/bjo.83.8.980
37 Phene S , Dunn RC , Hammel N , et al. Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs[J]. Ophthalmology, 2019, 126 (12): 1627- 1639.
doi: 10.1016/j.ophtha.2019.07.024
38 Asaoka R , Murata H , Hirasawa K , et al. Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images[J]. Am J Ophthalmol, 2019, 198: 136- 145.
doi: 10.1016/j.ajo.2018.10.007
39 Li F , Wang Z , Qu GX , et al. Automatic differentiation of glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network[J]. BMC Med Imaging, 2018, 18 (1): 35.
doi: 10.1186/s12880-018-0273-5
40 Rudnicka AR , Kapetanakis VV , Jarrar Z , et al. Incidence of late-stage age-related macular degeneration in American whites: systematic review and meta-analysis[J]. Am J ophthalmol, 2015, 160 (1): 85- 93.
doi: 10.1016/j.ajo.2015.04.003
41 Bressler NM . Age-related macular degeneration is the leading cause of blindness[J]. JAMA, 2004, 291 (15): 1900- 1901.
doi: 10.1001/jama.291.15.1900
42 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
43 Venhuizen FG , van Ginneken B , van Asten F , et al. Automated staging of age-related macular degeneration using optical coherence tomography[J]. Invest Ophthalmol Vis Sci, 2017, 58 (4): 2318- 2328.
doi: 10.1167/iovs.16-20541
44 Waldstein SM , Vogl WD , Bogunovic H , et al. Characterization of drusen and hyperreflective foci as biomarkers for disease progression in age-related macular degeneration using artificial intelligence in optical coherence tomography[J]. JAMA Ophthalmol, 2020, 138 (7): 740- 747.
doi: 10.1001/jamaophthalmol.2020.1376
45 Lee H , Kang KE , Chung H , et al. Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration[J]. Am J Ophthalmol, 2018, 191: 64- 75.
doi: 10.1016/j.ajo.2018.04.007
46 Liu YT, Yang JY, Zhou Y, et al. Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network[J]. Br J Ophthalmol, 2020: bjophthalmol-bjophtha2019-315338. doi:10.1136/bjophthalmol-2019-315338.
47 Li ZW , Guo C , Nie DY , et al. Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images[J]. Commun Biol, 2020, 3 (1): 15.
doi: 10.1038/s42003-019-0730-x
48 Brown JM , Campbell JP , Beers A , et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks[J]. JAMA Ophthalmol, 2018, 136 (7): 803- 810.
doi: 10.1001/jamaophthalmol.2018.1934
49 Nagasato D , Tabuchi H , Ohsugi H , et al. Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion[J]. Int J Ophthalmol, 2019, 12 (1): 94- 99.
50 Poplin R , Varadarajan AV , Blumer K , et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[J]. Nat Biomd Eng, 2018, 2 (3): 158- 164.
doi: 10.1038/s41551-018-0195-0
51 Miller DD , Brown EW . Artificial intelligence in medical practice: the question to the answer?[J]. Am J Med, 2018, 131 (2): 129- 133.
doi: 10.1016/j.amjmed.2017.10.035
52 张宁男楠, 张璋. 人工智能辅助教学在医学影像规范化培训中的新探索[J]. 教育教学论坛, 2019, (25): 176- 178.
ZHANG Ningnannan , ZHANG Zhang . New exploration of artificial intelligence assisted instruction in standardization training of residents[J]. Education Teaching Forum, 2019, (25): 176- 178.
53 李丽, 马琳. 人工智能技术在皮肤科教学中的应用[J]. 继续医学教育, 2018, 32 (11): 17- 18.
54 徐涛, 王洪祥, 龚振宇, 等. 人工智能在神经外科教学中的应用[J]. 中国继续医学教育, 2018, 10 (34): 38- 39.
XU Tao , WANG Hongxiang , GONG Zhenyu , et al. Application of artificial intelligence in neurosurgery teaching[J]. China Continuing Medical Education, 2018, 10 (34): 38- 39.
55 高蕾, 彭贤贵, 杨武晨, 等. 应用人工智能图像教学系统提高医学生骨髓细胞形态判读能力[J]. 中华医学教育探索杂志, 2020, 19 (5): 569- 573.
GAO Lei , PENG Xiangui , YANG Wuchen , et al. Application of artificial intelligence teaching-picture system to improve the bone marrow cell morphological reading ability of clinical medical students[J]. Chinese Journal of Medical Education Research, 2020, 19 (5): 569- 573.
56 Long EP , Lin HT , Liu ZZ , et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts[J]. Nat biomed Eng, 2017, 1 (2): 0024.
doi: 10.1038/s41551-016-0024
57 Zhang ML , Zhou ZH . A Review on multi-label learning algorithms[J]. IEEE Trans Knowl Data Eng, 2014, 26 (8): 1819- 1837.
doi: 10.1109/TKDE.2013.39
58 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]//Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu Hi USA. New York, NY, USA: ACM, 2020: 1-12.
59 Li WT , Yang YH , Zhang K , et al. Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders[J]. Nat Biomed Eng, 2020, 4 (8): 767- 777.
doi: 10.1038/s41551-020-0577-y
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