Application of artificial intelligence in the diagnosis imaging of diabetic retinopathy_Chinese Journal of Ocular Fundus Diseases

Authors：

Feng Siqi ,  Xu Xinrong

Department of Ophthalmology, Nanjing University of Traditional Chinese Medicine, Nanjing 210029, China;

Corresponding?author：

Xu Xinrong, Email: yfy133@njucm.edu.cn

Keywords：

Artificial intelligence; Diabetic retinopathy; Diagnostic imaging; Review

DOI：

10.3760/cma.j.cn511434-20250901-00370

Video：

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Abstract Full text Figures/Tables Video References Cited by

Diabetic retinopathy (DR) is a major cause of visual impairment among working-age populations. In recent years, artificial intelligence (AI) has demonstrated significant application value in DR diagnosis, leveraging core advantages such as high efficiency and low error rates. Currently, the technical system of AI in DR image diagnosis mainly includes links like image preprocessing, feature extraction, diverse algorithmic models, and dataset construction. In practical applications, AI models can achieve automated screening and grading diagnosis of DR images, enhance diagnostic efficiency by integrating multimodal technologies, and have been successfully applied to mobile devices; meanwhile, the development of explainable AI has further boosted the credibility of AI models. Currently, this field still faces challenges, including insufficient data quality and scale, limited model interpretability, inadequate clinical validation, ethical and privacy risks, and a lack of unified technical standards. In the future, with continuous technological breakthroughs and the establishment of standardized evaluation systems, the reliability and accessibility of AI in DR diagnosis will be further enhanced.

Citation： Feng Siqi, Xu Xinrong. Application of artificial intelligence in the diagnosis imaging of diabetic retinopathy. Chinese Journal of Ocular Fundus Diseases, 2026, 42(2): 174-180. doi: 10.3760/cma.j.cn511434-20250901-00370 Copy

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44.
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55. Aswal S, Ahuja NJ, Mehra R. Handling imbalance and limited data in thyroid ultrasound and diabetic retinopathy datasets using discrete levy flights grey wolf optimizer based random forest for robust medical data classification[J/OL]. ACM Trans Asian Low-Resour Lang Inf Process, 2024, 2024: e1-e25[2024-01-31]. https://doi.org/10.1145/3648363. DOI: 10.1145/3648363.
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58.
59. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges[J/OL]. Int J Environ Res Public Health, 2021, 18(1): 271[2021-10-29]. https://pubmed.ncbi.nlm.nih.gov/34753022/. DOI: 10.3390/ijerph18010271.
60. Mudgal SK, Agarwal R, Chaturvedi J, et al. Real-world application, challenges and implication of artificial intelligence in healthcare: an essay[J/OL]. Pan Afr Med J, 2022, 43: 3[2022-09-02]. https://pubmed.ncbi.nlm.nih.gov/36284890/. DOI: 10.11604/pamj.2022.43.3.33384.

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