Research progress on the application of artificial intelligence in the screening and treatment of retinopathy of prematurity_Chinese Journal of Ocular Fundus Diseases

Authors：

Wu Di ^1,2 , Mao Jianbo ³ , Lu Yu ⁴ , Xu Xiaorong ^1,2 , Shen Lijun ^3,5 ,  Sun Mingzhai ²

1. University of Science and Technology of China, Hefei 230026, China;
2. Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China;
3. Department of Ophthalmology, Zhejiang Provincial People's Hospital, Hangzhou 310000, China;
4. Hefei Orbis Biotech LTD, Hefei 230000, China;
5. Eye Hospital of Wenzhou Medical University, Wenzhou 325000, China;

Corresponding author：

Sun Mingzhai, Email: mingzhai@ustc.edu.cn

Keywords：

Retinopathy of prematurity; Medical image analysis; Artificial intelligence; Deep learning; Review

DOI：

10.3760/cma.j.cn511434-20230109-00013

Video：

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Abstract Full text Figures/Tables Video References Cited by

Retinopathy of prematurity (ROP) is a major cause of vision loss and blindness among premature infants. Timely screening, diagnosis, and intervention can effectively prevent the deterioration of ROP. However, there are several challenges in ROP diagnosis globally, including high subjectivity, low screening efficiency, regional disparities in screening coverage, and severe shortage of pediatric ophthalmologists. The application of artificial intelligence (AI) as an assistive tool for diagnosis or an automated method for ROP diagnosis can improve the efficiency and objectivity of ROP diagnosis, expand screening coverage, and enable automated screening and quantified diagnostic results. In the global environment that emphasizes the development and application of medical imaging AI, developing more accurate diagnostic networks, exploring more effective AI-assisted diagnosis methods, and enhancing the interpretability of AI-assisted diagnosis, can accelerate the improvement of AI policies of ROP and the implementation of AI products, promoting the development of ROP diagnosis and treatment.

Citation： Wu Di, Mao Jianbo, Lu Yu, Xu Xiaorong, Shen Lijun, Sun Mingzhai. Research progress on the application of artificial intelligence in the screening and treatment of retinopathy of prematurity. Chinese Journal of Ocular Fundus Diseases, 2023, 39(12): 1022-1027. doi: 10.3760/cma.j.cn511434-20230109-00013 Copy

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11. Teo ZL, Tham YC, Yu M, et al. Do we have enough ophthalmologists to manage vision-threatening diabetic retinopathy? A global perspective[J]. Eye, 2020, 34(7): 1255-1261. DOI: 10.1038/s41433-020-0776-5.
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13. Ahuja AS, Wagner IV, Dorairaj S, et al. Artificial intelligence in ophthalmology: a multidisciplinary approach[J/OL]. Integr Med Res, 2022, 11(4): 100888[2022-09-20]. https://pubmed.ncbi.nlm.nih.gov/36212633/. DOI: 10.1016/j.imr.2022.100888.
14. Goutam B, Hashmi MF, Geem ZW, et al. A comprehensive review of deep learning strategies in retinal disease diagnosis using fundus images[J]. IEEE Access, 2022, 10: 57796-57823. DOI: 10.1109/access.2022.3178372.
15. Chiang MF, Quinn GE, Fielder AR, et al. International classification of retinopathy of prematurity, third edition[J/OL]. Ophthalmology, 2021, 128(10): e51-e68[2021-07-08]. https://pubmed.ncbi.nlm.nih.gov/34247850/. DOI: 10.1016/j.ophtha.2021.05.031.
16. 黎彪, 丁雅珺, 邵毅. 人工智能在小儿眼科领域的应用研究进展[J]. 国际眼科杂志, 2020, 20(8): 1363-1366. DOI: 10.3980/j.issn.1672-5123.2020.8.14.Li B, Ding YJ, Shao Y. Research progress on application of artificial intelligence in pediatric ophthalmology[J]. Int Eye Sci, 2020, 20(8): 1363-1366. DOI: 10.3980/j.issn.1672-5123.2020.8.14.
17. 王慧冉. 早产儿视网膜病变发生及筛查现状[J]. 中国妇幼卫生杂志, 2022, 13(2): 1-5. DOI: 10.19757/j.cnki.issn1674-7763.2022.02.001.Wang HR. Status of the occurrence and screening of retinopathy of prematurity[J]. Chin J Women Child Health, 2022, 13(2): 1-5. DOI: 10.19757/j.cnki.issn1674-7763.2022.02.001.
18. Ells AL, Holmes JM, Astle WF, et al. Telemedicine approach to screening for severe retinopathy of prematurity: a pilot study[J]. Ophthalmology, 2003, 110(11): 2113-2117. DOI: 10.1016/S0161-6420(03)00831-5.
19. Fijalkowski N, Zheng LL, Henderson MT, et al. Stanford University network for diagnosis of retinopathy of prematurity (SUNDROP): five years of screening with telemedicine[J]. Ophthalmic Surg Lasers Imaging Retina, 2014, 45(2): 106-113. DOI: 10.3928/23258160-20140122-01.
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