THE USE OF ARTIFICIAL INTELLIGENCE IN DIABETIC RETINOPATHY SCREENING: ADVANCES, APPLICATIONS, AND FUTURE PERSPECTIVES
DOI:
https://doi.org/10.56238/sevened2026.015-058Keywords:
Diabetic Retinopathy, Artificial Intelligence, ScreeningAbstract
Diabetic retinopathy is one of the main microvascular complications of diabetes mellitus and represents one of the most frequent causes of preventable vision loss in adults of working age worldwide. The development of the disease is related to the progressive impairment of the retinal blood vessels, and it can evolve silently for long periods, reinforcing the importance of early diagnosis and regular ophthalmological follow-up (GOMES et al., 2015).
References
1. GULSHAN, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, Chicago, v. 316, n. 22, p. 2402–2410, 2016. Disponível em: https://jamanetwork.com/journals/jama/fullarticle/2588763. Acesso em: 16 maio 2026.
2. ABRAMOFF, M. D. et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Medicine, London, v. 1, n. 39, 2018. Disponível em: https://www.nature.com/articles/s41746-018-0040-6. Acesso em: 16 maio 2026.
3. VUJOSEVIC, S. et al. Novel artificial intelligence for diabetic retinopathy and diabetic macular edema: a review. Current Opinion in Ophthalmology, Philadelphia, v. 35, n. 6, p. 432–439, 2024. Disponível em: https://journals.lww.com/co-ophthalmology/fulltext/2024/11000/novel_artificial_intelligence_for_diabetic.7.aspx. Acesso em: 16 maio 2026.
4. FARAHAT, Z. et al. Diabetic retinopathy screening through artificial intelligence: current perspectives and future directions. Diagnostics, Basel, v. 14, n. 13, 2024. Disponível em: https://www.mdpi.com/2075-4418/14/13/1385. Acesso em: 16 maio 2026.
5. ALQAHTANI, A. S. et al. The efficacy of artificial intelligence in diabetic retinopathy screening: systematic review and meta-analysis. International Journal of Retina and Vitreous, London, v. 11, n. 1, 2025. Disponível em: https://pmc.ncbi.nlm.nih.gov/articles/PMC12012971/. Acesso em: 16 maio 2026.
6. WANG, T. W. et al. Systematic review and meta-analysis of regulator-approved artificial intelligence systems for diabetic retinopathy detection. NPJ Digital Medicine, London, v. 8, 2025. Disponível em: https://www.nature.com/articles/s41746-025-02223-8. Acesso em: 16 maio 2026.
7. XU, X. et al. The application of artificial intelligence in diabetic retinopathy. Frontiers in Cell and Developmental Biology, Lausanne, v. 12, 2024. Disponível em: https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2024.1473176/full. Acesso em: 16 maio 2026.
8. SENAPATI, A. et al. Artificial intelligence for diabetic retinopathy detection: a systematic review of deep learning approaches. Smart Health, Amsterdam, v. 35, 2024. Disponível em: https://www.sciencedirect.com/science/article/pii/S2352914824000017. Acesso em: 16 maio 2026.
9. ZAIER, F. et al. Artificial intelligence for diabetic retinopathy screening. European Journal of Public Health, Oxford, v. 35, Suppl. 1, 2025. Disponível em: https://pmc.ncbi.nlm.nih.gov/articles/PMC12555487/. Acesso em: 16 maio 2026.
10. BEALS, D. et al. Integrating AI-based retinal imaging in primary care: improving diabetic retinopathy screening and prevention of blindness. Health Systems & Reform, Philadelphia, v. 11, n. 1, 2025. Disponível em: https://www.tandfonline.com/doi/full/10.1080/28338073.2024.2437294. Acesso em: 16 maio 2026.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
