Inteligencia artificial en la prevención y detección temprana de la retinopatía diabética/ Artificial intelligence in the prevention and early detection of diabetic retinopathy/ Inteligência artificial na prevenção e detecção precoce da retinopatia diabética

Autores

  • Kevin Enmanuel García Vanegas Universidad Central de Nicaragua
  • Sheila Karina Valdivia Quiroz Universidad Autónoma de Nicaragua

DOI:

https://doi.org/10.30681/2526101014437

Palavras-chave:

Inteligencia Artificial, Diabetes Mellitus, Retinopatía Diabética, Diagnóstico Precoz, Aprendizaje Automático

Resumo

EDITORIAL

Biografia do Autor

  • Kevin Enmanuel García Vanegas, Universidad Central de Nicaragua

    Médico y cirujano general. Maestrante en Salud Pública en Universidad Autónoma de Nicaragua CIES. Coordinador de la carrera de Medicina. Universidad Central de Nicaragua

  • Sheila Karina Valdivia Quiroz, Universidad Autónoma de Nicaragua

    Médico y cirujano general. Doctorado en Ciencias de la Salud en Universidad Autónoma de Nicaragua CIES

Referências

1. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016; 316(22):2402‑10.

2. Ting DSW, Cheung CY‑L, Lim G, Tan GSW, Quang ND, Gan A, 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. JAMA. 2017; 318(22):2211‑23.

3. World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: WHO; 2021.

4. Voets M, Møllersen K, Bongo LA. Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. ArXiv. 2018. Available from: https://arxiv.org/abs/1803.04337.

5. World Health Organization. WHO Consultation towards the development of guidance on ethics and governance of artificial intelligence for health. Geneva: WHO; 2019.

6. Aparicio-Montenegro PR, Narro Andrade MG, León-Velarde CG, Morales Romero GP, Fernández-Flores SM. Modelos predictivos en la Salud Pública: El abordaje de la diabetes mediante la Inteligencia Artificial. Cuestiones Políticas. 2025; 43(82):91-106.

7. Nielsen KB, Lautrup ML, Andersen JKH, Savarimuthu TR, Grauslund J. Deep learning–based algorithms in screening of diabetic retinopathy: a systematic review of diagnostic performance. Ophthalmol Retina. 2019; 3(4):294-304.

8. Rahmati M, Smith L, Boyer L, Fond G, Yon DK, Lee H, et al. Artificial Intelligence improves follow-up appointment uptake for diabetic retinal assessment: a systematic review and meta-analysis. Eye. 2025; 39:2398–2406.

9. Li Y, Jin N, Zhan Q, Huang Y, Sun A, Yin F, et al. Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis. Front Endocrinol (Lausanne). 2025; 16:1495306.

10. Wang Z, Li Z, Li K, Mu S, Zhou X, Di Y. Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies. Front Endocrinol (Lausanne). 2023; 14:1197783.

11. Asserfelt R. La Inteligencia Artificial puede detectar enfermedades oculares con la misma precisión que algunos de los principales expertos [Internet]. RetinaLyze España S.L.; 2022 Jul [citado 17 Nov 2025]. Disponible en: https://www.imopticas.es/uploads/2022/07/inteligencia_artificial_puede_1722_20220721111252.pdf.

Publicado

2026-05-01

Como Citar

García Vanegas, K. E., & Valdivia Quiroz, S. K. (2026). Inteligencia artificial en la prevención y detección temprana de la retinopatía diabética/ Artificial intelligence in the prevention and early detection of diabetic retinopathy/ Inteligência artificial na prevenção e detecção precoce da retinopatia diabética. Journal Health NPEPS, 11(1). https://doi.org/10.30681/2526101014437