Issues related to artificial intelligence research in ophthalmology_Chinese Journal of Ocular Fundus Diseases

Authors：

 Chen Youxin , Feng Shi , Zhao Qing

Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100730, China;

Corresponding author：

Chen Youxin, Email: chenyx@pumch.cn

Keywords：

Artificial intelligence; Diagnostic techniques, ophthalmological; Image interpretation, computer-assisted; Editorial

DOI：

10.3760/cma.j.cn511434-20220210-00077

Video：

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At present, artificial intelligence (AI) has been widely used in the diagnosis and treatment of various ophthalmological diseases, but there are still many problems. Due to the lack of standardized test sets, gold standards, and recognized evaluation systems for the accuracy of AI products, it is difficult to compare the results of multiple studies. When it comes to the field of image generation, we hardly have an efficient approach to evaluating research results. In clinical practice, ophthalmological AI research is often out of touch with actual clinical needs. The requirements for the quality and quantity of clinical data put more burden on AI research, limiting the transformation of AI studies. The prediction of systemic diseases based on fundus images is making progressive advancement. However, the lack of interpretability of the research lower the acceptance. Ophthalmology AI research also suffer from ethical controversy due to unconstructed regulations and regulatory mechanisms, concerns on patients’ privacy and data security, and the risk of aggravating the unfairness of medical resources.

Citation： Chen Youxin, Feng Shi, Zhao Qing. Issues related to artificial intelligence research in ophthalmology. Chinese Journal of Ocular Fundus Diseases, 2022, 38(2): 89-92. doi: 10.3760/cma.j.cn511434-20220210-00077 Copy

1.	Ting DSJ, Foo VH, Yang LWY, et al. Artificial intelligence for anterior segment diseases: emerging applications in ophthalmology[J]. Br J Ophthalmol, 2021, 105(2): 158-168. DOI: 10.1136/bjophthalmol-2019-315651.
2.	Lin D, Xiong J, Liu C, et al. Application of comprehensive artificial intelligence retinal expert (CARE) system: a national real-world evidence study[J/OL]. Lancet Digit Health, 2021, 3(8): e486-e495[2021-08-01]. https://pubmed.ncbi.nlm.nih.gov/34325853/. DOI: 10.1016/S2589-7500(21)00086-8.
3.	Li Z, Guo C, Nie D, 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.
4.	Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: a review[J]. Clin Exp Ophthalmol, 2019, 47(1): 128-139. DOI: 10.1111/ceo.13381.
5.	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: 10.1136/bjophthalmol-2018-313173.
6.	Krause J, Gulshan V, Rahimy E, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy[J]. Ophthalmology, 2018, 125(8): 1264-1272. DOI: 10.1016/j.ophtha.2018.01.034.
7.	Liu Y, Yang J, 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, 104(12): 1735-1740. DOI: 10.1136/bjophthalmol-2019-315338.
8.	Chen JH, Asch SM. Machine learning and prediction in medicine-beyond the peak of inflated expectations[J]. N Engl J Med, 2017, 376(26): 2507-2509. DOI: 10.1056/NEJMp1702071.
9.	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.
10.	Chen JH, Alagappan M, Goldstein MK, et al. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets[J]. Int J Med Inform, 2017, 102: 71-79. DOI: 10.1016/j.ijmedinf.2017.03.006.
11.	Chang J, Ko A, Park SM, et al. Association of cardiovascular mortality and deep learning-funduscopic atherosclerosis score derived from retinal fundus images[J]. Am J Ophthalmol, 2020, 217: 121-130. DOI: 10.1016/j.ajo.2020.03.027.
12.	Rim TH, Lee CJ, Tham YC, et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs[J/OL]. Lancet Digit Health, 2021, 3(5): e306-e316[2021-05-01]. https://pubmed.ncbi.nlm.nih.gov/33890578/. DOI: 10.1016/S2589-7500(21)00043-1.
13.	Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[J]. Nat Biomed Eng, 2018, 2(3): 158-164. DOI: 10.1038/s41551-018-0195-0.
14.	Rim TH, Lee G, Kim Y, et al. Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms[J/OL]. Lancet Digit Health, 2020, 2(10): e526-e536[2020-10-01]. https://pubmed.ncbi.nlm.nih.gov/33328047/. DOI: 10.1016/S2589-7500(20)30216-8.
15.	Tian J, Smith G, Guo H, et al. Modular machine learning for Alzheimer's disease classification from retinal vasculature[J/OL]. Sci Rep, 2021, 11(1): 238[2021-01-08]. https://pubmed.ncbi.nlm.nih.gov/33420208/. DOI: 10.1038/s41598-020-80312-2.
16.	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.

1. Ting DSJ, Foo VH, Yang LWY, et al. Artificial intelligence for anterior segment diseases: emerging applications in ophthalmology[J]. Br J Ophthalmol, 2021, 105(2): 158-168. DOI: 10.1136/bjophthalmol-2019-315651.
2. Lin D, Xiong J, Liu C, et al. Application of comprehensive artificial intelligence retinal expert (CARE) system: a national real-world evidence study[J/OL]. Lancet Digit Health, 2021, 3(8): e486-e495[2021-08-01]. https://pubmed.ncbi.nlm.nih.gov/34325853/. DOI: 10.1016/S2589-7500(21)00086-8.
3. Li Z, Guo C, Nie D, 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.
4. Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: a review[J]. Clin Exp Ophthalmol, 2019, 47(1): 128-139. DOI: 10.1111/ceo.13381.
5. 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: 10.1136/bjophthalmol-2018-313173.
6. Krause J, Gulshan V, Rahimy E, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy[J]. Ophthalmology, 2018, 125(8): 1264-1272. DOI: 10.1016/j.ophtha.2018.01.034.
7. Liu Y, Yang J, 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, 104(12): 1735-1740. DOI: 10.1136/bjophthalmol-2019-315338.
8. Chen JH, Asch SM. Machine learning and prediction in medicine-beyond the peak of inflated expectations[J]. N Engl J Med, 2017, 376(26): 2507-2509. DOI: 10.1056/NEJMp1702071.
9. 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.
10. Chen JH, Alagappan M, Goldstein MK, et al. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets[J]. Int J Med Inform, 2017, 102: 71-79. DOI: 10.1016/j.ijmedinf.2017.03.006.
11. Chang J, Ko A, Park SM, et al. Association of cardiovascular mortality and deep learning-funduscopic atherosclerosis score derived from retinal fundus images[J]. Am J Ophthalmol, 2020, 217: 121-130. DOI: 10.1016/j.ajo.2020.03.027.
12. Rim TH, Lee CJ, Tham YC, et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs[J/OL]. Lancet Digit Health, 2021, 3(5): e306-e316[2021-05-01]. https://pubmed.ncbi.nlm.nih.gov/33890578/. DOI: 10.1016/S2589-7500(21)00043-1.
13. Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[J]. Nat Biomed Eng, 2018, 2(3): 158-164. DOI: 10.1038/s41551-018-0195-0.
14. Rim TH, Lee G, Kim Y, et al. Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms[J/OL]. Lancet Digit Health, 2020, 2(10): e526-e536[2020-10-01]. https://pubmed.ncbi.nlm.nih.gov/33328047/. DOI: 10.1016/S2589-7500(20)30216-8.
15. Tian J, Smith G, Guo H, et al. Modular machine learning for Alzheimer's disease classification from retinal vasculature[J/OL]. Sci Rep, 2021, 11(1): 238[2021-01-08]. https://pubmed.ncbi.nlm.nih.gov/33420208/. DOI: 10.1038/s41598-020-80312-2.
16. 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.

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