Applications of artificial intelligence in the diagnosis of eye diseases_Chinese Journal of Ocular Fundus Diseases

Authors：

Tong Yan , Lu Wei , Xing Yiqiao ,  Shen Yin

Eye Centre, Renmin Hospital of Wuhan University, Wuhan 430060, China;

Corresponding author：

Shen Yin, Email: yinshen@whu.edu.cn

Keywords：

Artificial intelligence; Diagnostic techniques, ophthalmological; Review

DOI：

10.3760/cma.j.issn.1005-1015.2019.05.019

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Ophthalmic imaging examination is the main basis for early screening, evaluation and diagnosis of eye diseases. In recent years, with the improvement of computer data analysis ability, the deepening of new algorithm research and the popularization of big data platform, artificial intelligence (AI) technology has developed rapidly and become a hot topic in the field of medical assistant diagnosis. The advantage of AI is accurate and efficient, which has great application value in processing image-related data. The application of AI not only helps to promote the development of AI research in ophthalmology, but also helps to establish a new medical service model for ophthalmic diagnosis and promote the process of prevention and treatment of blindness. Future research of ophthalmic AI should use multi-modal imaging data comprehensively to diagnose complex eye diseases, integrate standardized and high-quality data resources, and improve the performance of algorithms.

Citation： Tong Yan, Lu Wei, Xing Yiqiao, Shen Yin. Applications of artificial intelligence in the diagnosis of eye diseases. Chinese Journal of Ocular Fundus Diseases, 2019, 35(5): 506-509. doi: 10.3760/cma.j.issn.1005-1015.2019.05.019 Copy

1.	Leachman SA, Merlino G. The final frontier in cancer diagnosis[J]. Nature, 2017, 542(7639): 36-38. DOI: 10.1038/nature21492.
2.	Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. DOI: 10.1023/a:1010933404324.
3.	Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297. DOI: 10.1007/bf00994018.
4.	LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.
5.	郑志. 糖尿病视网膜病变临床防治: 进展、挑战与展望[J]. 中华眼底病杂志, 2012, 28(3): 209-213. DOI: 10.3760/cma.j.issn.1005-1015.2012.03.001.Zheng Z. Prevention and treatment of diabetic retinopathy: progress, challenges and future prospects[J]. Chin J Ocul Fundus Dis, 2012, 28(3): 209-213. DOI: 10.3760/cma.j.issn.1005-1015.2012.03.001.
6.	Wang S, Tang HL, Al turk LI, et al. Localizing microaneurysms in fundus images through singular spectrum analysis[J]. IEEE Trans Biomed Eng, 2017, 64(5): 990-1002. DOI: 10.1109/tbme.2016.2585344.
7.	Akram MU, Tariq A, Khan SA, et al. Automated detection of exudates and macula for grading of diabetic macular edema[J]. Comput Methods Programs Biomed, 2014, 114(2): 141-152. DOI: 10.1016/j.cmpb.2014.01.010.
8.	Hassan T, Usman Akram MU, Hassan B, et al. Automated segmentation of subretinal layers for the detection of macular edema[J]. Appl Opt, 2016, 55(3): 454-461. DOI: 10.1364/AO.55.000454.
9.	Yu S, Xiao D, Kanagasingam Y. Automatic detection of neovascularization on optic disk region with feature extraction and support vector machine[J]. Conf Proc IEEE Eng Med Biol Soc, 2016, 2016: 1324-1327. DOI: 10.1109/EMBC.2016.7590951.
10.	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.
11.	Mori K, Takahashi H, Tampo H, et al. Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy[J/OL]. PLoS One, 2017, 12(6): 0179790[2017-01-22]. https://doi.org/10.1371/journal.pone.0179790. DOI: 10.1371/journal.pone.0179790.
12.	Abbas Q, Fondon I, Sarmiento A, et al. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features[J]. Med Biol Eng Comput, 2017, 55(11): 1959-1974. DOI: 10.1007/s11517-017-1638-6.
13.	Hwang TS, Gao SS, Liu L, et al. Automated quantification of capillary nonperfusion using optical coherence tomography angiography in diabetic retinopathy[J]. JAMA Ophthalmol, 2016, 134(4): 367-373. DOI: 10.1001/jamaophthalmol.2015.5658.
14.	ElTanboly A, Ismail M, Shalaby A, et al. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images[J]. Medical Physics, 2017, 44(3): 914-923. DOI: 10.1002/mp.12071.
15.	De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nat Med, 2018, 24(9): 1342-1350. DOI: 10.1038/s41591-018-0107-6.
16.	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 Digital Med, 2018, 1: 39. DOI: 10.1038/s41746-018-0040-6.
17.	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.
18.	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): 141-150. DOI: 10.1167/iovs.17-21789.
19.	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.
20.	白玉婧, 黎晓新. 新生血管性老年性黄斑变性药物治疗面临的挑战与未来的发展趋势[J]. 中华眼底病杂志, 2016, 32(1): 3-7. DOI: 10.3760/cma.j.issn.1005-1015.2016.01.002.Bai YJ, Li XX. Progression and challenge of therapeutic strategies in neovascular age-related macular degeneration[J]. Chin J Ocul Fundus Dis, 2016, 32(1): 3-7. DOI: 10.3760/cma.j.issn.1005-1015.2016.01.002.
21.	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, 2017, 256(1): 91-98. DOI: 10.1007/s00417-017-3839-y.
22.	Caixinha M, Amaro J, Santos M, et al. In-vivo automatic nuclear cataract detection and classification in an animal model by ultrasounds[J]. IEEE Trans Biomed Eng, 2016, 63(11): 2326-2335. DOI: 10.1109/tbme.2016.2527787.
23.	Mohammadi SF, Sabbaghi M, Z-Mehrjardi H, et al. Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification[J]. J Cataract Refract Surg, 2012, 38(3): 403-408. DOI: 10.1016/j.jcrs.2011.09.036.
24.	Gao X, Lin S,Wong TY. Automatic feature learning to grade nuclear cataracts based on deep learning[J]. IEEE Trans Biomed Eng, 2015, 62(11): 2693-2701. DOI: 10.1109/tbme.2015.2444389.
25.	Liu X, Jiang J, Zhang K, et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network[J/OL]. PLoS One, 2017, 12(3): 0168606[2017-03-17]. https://doi.org/10.1371/journal.pone.0168606. DOI: 10.1371/journal.pone.0168606.
26.	Singh A, Dutta MK, ParthaSarathi M, et al. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image[J]. Comput Methods Programs Biomed, 2016, 124: 108-120. DOI: 10.1016/j.cmpb.2015.10.010.
27.	Li AN, Cheng J, Wong DWK, et al. Integrating holistic and local deep features for glaucoma classification[J]. Conf Proc IEEE Eng Med Biol Soc, 2016, 2016: 1328-1331. DOI: 10.1109/EMBC.2016.7590952.
28.	Bizios D, Heijl A, Hougaard JL, et al. Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT[J]. Acta Ophthalmol, 2010, 88(1): 44-52. DOI: 10.1111/j.1755-3768.2009.01784.x.
29.	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://dx.doi.org/10.1155/2013/789129. DOI: 10.1155/2013/789129.
30.	Kim SJ, Cho KJ, Oh S.et al. Development of machine learning models for diagnosis of glaucoma[J/OL]. PLoS One, 2017, 12(5): 0177726[2017-05-23]. https://doi.org/10.1371/journal.pone.0177726. DOI: 10.1371/journal.pone.0177726.
31.	Andersson S, Heijl A, Bizios D, et al. Comparison of clinicians and an artificial neural network regarding accuracy and certainty in performance of visual field assessment for the diagnosis of glaucoma[J]. Acta Ophthalmol, 2013, 91(5): 413-417. DOI: 10.1111/j.1755-3768.2012.02435.x.
32.	Asaoka R, Murata H, Iwase A, et al. Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier[J]. Ophthalmology, 2016, 123(9): 1974-1980. DOI: 10.1016/j.ophtha.2016.05.029.
33.	Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118. DOI: 10.1038/nature21056.
34.	Bejnordi BE, Zuidhof G, Balkenhol M, et al. Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images[J/OL]. J Med Imaging (Bellingham), 2017, 4(4): 044504[2017-12-14]. https://doi.org/10.1117/1.JMI.4.4.044504. DOI: 10.1117/1.Jmi.4.4.044504.
35.	Ambastha AK, Leong TY, Alzheimer's Disease Neuroimaging Initiative. A deep learning approach to neuroanatomical characterisation of Alzheimer's disease[J]. Stud Health Technol Inform, 2017, 245: 1249.
36.	Weng SF, Reps J, Kai J, et al. Can machine-learning improve cardiovascular risk prediction using routine clinical data?[J/OL]. PLoS One, 2017, 12(4): 174944[2017-04-04]. https://doi.org/10.1371/journal.pone.0174944. DOI: 10.1371/journal.pone.0174944.
37.	陈有信, 张碧磊, 张弘哲. 眼科人工智能技术的现状与问题[J]. 中华眼底病杂志, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.Cheng YX, Zhang BL, Zhang HZ. Insights and prospectives of ophthalmologic artificial intelligence technology[J]. Chin J Ocul Fundus Dis, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.

1. Leachman SA, Merlino G. The final frontier in cancer diagnosis[J]. Nature, 2017, 542(7639): 36-38. DOI: 10.1038/nature21492.
2. Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. DOI: 10.1023/a:1010933404324.
3. Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297. DOI: 10.1007/bf00994018.
4. LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.
5. 郑志. 糖尿病视网膜病变临床防治: 进展、挑战与展望[J]. 中华眼底病杂志, 2012, 28(3): 209-213. DOI: 10.3760/cma.j.issn.1005-1015.2012.03.001.Zheng Z. Prevention and treatment of diabetic retinopathy: progress, challenges and future prospects[J]. Chin J Ocul Fundus Dis, 2012, 28(3): 209-213. DOI: 10.3760/cma.j.issn.1005-1015.2012.03.001.
6. Wang S, Tang HL, Al turk LI, et al. Localizing microaneurysms in fundus images through singular spectrum analysis[J]. IEEE Trans Biomed Eng, 2017, 64(5): 990-1002. DOI: 10.1109/tbme.2016.2585344.
7. Akram MU, Tariq A, Khan SA, et al. Automated detection of exudates and macula for grading of diabetic macular edema[J]. Comput Methods Programs Biomed, 2014, 114(2): 141-152. DOI: 10.1016/j.cmpb.2014.01.010.
8. Hassan T, Usman Akram MU, Hassan B, et al. Automated segmentation of subretinal layers for the detection of macular edema[J]. Appl Opt, 2016, 55(3): 454-461. DOI: 10.1364/AO.55.000454.
9. Yu S, Xiao D, Kanagasingam Y. Automatic detection of neovascularization on optic disk region with feature extraction and support vector machine[J]. Conf Proc IEEE Eng Med Biol Soc, 2016, 2016: 1324-1327. DOI: 10.1109/EMBC.2016.7590951.
10. 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.
11. Mori K, Takahashi H, Tampo H, et al. Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy[J/OL]. PLoS One, 2017, 12(6): 0179790[2017-01-22]. https://doi.org/10.1371/journal.pone.0179790. DOI: 10.1371/journal.pone.0179790.
12. Abbas Q, Fondon I, Sarmiento A, et al. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features[J]. Med Biol Eng Comput, 2017, 55(11): 1959-1974. DOI: 10.1007/s11517-017-1638-6.
13. Hwang TS, Gao SS, Liu L, et al. Automated quantification of capillary nonperfusion using optical coherence tomography angiography in diabetic retinopathy[J]. JAMA Ophthalmol, 2016, 134(4): 367-373. DOI: 10.1001/jamaophthalmol.2015.5658.
14. ElTanboly A, Ismail M, Shalaby A, et al. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images[J]. Medical Physics, 2017, 44(3): 914-923. DOI: 10.1002/mp.12071.
15. De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nat Med, 2018, 24(9): 1342-1350. DOI: 10.1038/s41591-018-0107-6.
16. 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 Digital Med, 2018, 1: 39. DOI: 10.1038/s41746-018-0040-6.
17. 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.
18. 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): 141-150. DOI: 10.1167/iovs.17-21789.
19. 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.
20. 白玉婧, 黎晓新. 新生血管性老年性黄斑变性药物治疗面临的挑战与未来的发展趋势[J]. 中华眼底病杂志, 2016, 32(1): 3-7. DOI: 10.3760/cma.j.issn.1005-1015.2016.01.002.Bai YJ, Li XX. Progression and challenge of therapeutic strategies in neovascular age-related macular degeneration[J]. Chin J Ocul Fundus Dis, 2016, 32(1): 3-7. DOI: 10.3760/cma.j.issn.1005-1015.2016.01.002.
21. 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, 2017, 256(1): 91-98. DOI: 10.1007/s00417-017-3839-y.
22. Caixinha M, Amaro J, Santos M, et al. In-vivo automatic nuclear cataract detection and classification in an animal model by ultrasounds[J]. IEEE Trans Biomed Eng, 2016, 63(11): 2326-2335. DOI: 10.1109/tbme.2016.2527787.
23. Mohammadi SF, Sabbaghi M, Z-Mehrjardi H, et al. Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification[J]. J Cataract Refract Surg, 2012, 38(3): 403-408. DOI: 10.1016/j.jcrs.2011.09.036.
24. Gao X, Lin S,Wong TY. Automatic feature learning to grade nuclear cataracts based on deep learning[J]. IEEE Trans Biomed Eng, 2015, 62(11): 2693-2701. DOI: 10.1109/tbme.2015.2444389.
25. Liu X, Jiang J, Zhang K, et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network[J/OL]. PLoS One, 2017, 12(3): 0168606[2017-03-17]. https://doi.org/10.1371/journal.pone.0168606. DOI: 10.1371/journal.pone.0168606.
26. Singh A, Dutta MK, ParthaSarathi M, et al. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image[J]. Comput Methods Programs Biomed, 2016, 124: 108-120. DOI: 10.1016/j.cmpb.2015.10.010.
27. Li AN, Cheng J, Wong DWK, et al. Integrating holistic and local deep features for glaucoma classification[J]. Conf Proc IEEE Eng Med Biol Soc, 2016, 2016: 1328-1331. DOI: 10.1109/EMBC.2016.7590952.
28. Bizios D, Heijl A, Hougaard JL, et al. Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT[J]. Acta Ophthalmol, 2010, 88(1): 44-52. DOI: 10.1111/j.1755-3768.2009.01784.x.
29. 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://dx.doi.org/10.1155/2013/789129. DOI: 10.1155/2013/789129.
30. Kim SJ, Cho KJ, Oh S.et al. Development of machine learning models for diagnosis of glaucoma[J/OL]. PLoS One, 2017, 12(5): 0177726[2017-05-23]. https://doi.org/10.1371/journal.pone.0177726. DOI: 10.1371/journal.pone.0177726.
31. Andersson S, Heijl A, Bizios D, et al. Comparison of clinicians and an artificial neural network regarding accuracy and certainty in performance of visual field assessment for the diagnosis of glaucoma[J]. Acta Ophthalmol, 2013, 91(5): 413-417. DOI: 10.1111/j.1755-3768.2012.02435.x.
32. Asaoka R, Murata H, Iwase A, et al. Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier[J]. Ophthalmology, 2016, 123(9): 1974-1980. DOI: 10.1016/j.ophtha.2016.05.029.
33. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118. DOI: 10.1038/nature21056.
34. Bejnordi BE, Zuidhof G, Balkenhol M, et al. Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images[J/OL]. J Med Imaging (Bellingham), 2017, 4(4): 044504[2017-12-14]. https://doi.org/10.1117/1.JMI.4.4.044504. DOI: 10.1117/1.Jmi.4.4.044504.
35. Ambastha AK, Leong TY, Alzheimer's Disease Neuroimaging Initiative. A deep learning approach to neuroanatomical characterisation of Alzheimer's disease[J]. Stud Health Technol Inform, 2017, 245: 1249.
36. Weng SF, Reps J, Kai J, et al. Can machine-learning improve cardiovascular risk prediction using routine clinical data?[J/OL]. PLoS One, 2017, 12(4): 174944[2017-04-04]. https://doi.org/10.1371/journal.pone.0174944. DOI: 10.1371/journal.pone.0174944.
37. 陈有信, 张碧磊, 张弘哲. 眼科人工智能技术的现状与问题[J]. 中华眼底病杂志, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.Cheng YX, Zhang BL, Zhang HZ. Insights and prospectives of ophthalmologic artificial intelligence technology[J]. Chin J Ocul Fundus Dis, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.

Previous Article
类似视网膜血管炎表现的转移性眼内淋巴瘤一例
Next Article
Research progress on morphology and associations of peripapillary atrophy

Chinese Journal of Ocular Fundus Diseases

Applications of artificial intelligence in the diagnosis of eye diseases

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content