Perfil de riesgo cardiovascular y rendimiento de modelos de inteligencia artificial para la detección de retinopatía diabética e hipertensiva en el Caribe Colombiano: un estudio transversal
| datacite.rights | http://purl.org/coar/access_right/c_f1cf | |
| dc.contributor.advisor | Urina Triana, Manuel | |
| dc.contributor.advisor | Urina Triana, Miguel | |
| dc.contributor.advisor | Orozco Acosta, Erick | |
| dc.contributor.advisor | Scaf Jaraba, Luis | |
| dc.contributor.author | Pimienta Redondo, Camilo Andrés | |
| dc.date.accessioned | 2026-02-09T16:53:50Z | |
| dc.date.available | 2026-02-09T16:53:50Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | La prevalencia de diabetes mellitus e hipertensión se encuentra en creciente aumento siendo importante determinar la características y métodos diagnósticos para valoración de los pacientes que presentan retinopatía diabética (RD) e hipertensiva (RH) para prevención de progresión de la enfermedad. Métodos: Se realizó un estudio transversal descriptivo para identificar las características epidemiológicas y clínicas de pacientes con retinopatía hipertensiva y/o diabética de la consulta de retina de Centros Oftalmológicos del Caribe colombiano durante el periodo comprendido entre octubre del año 2017 y octubre del año 2019. La población quedó constituida por 219 pacientes. Se evaluó el desempeño diagnóstico de dos modelos de inteligencia artificial (IA) calculando certeza diagnóstica, sensibilidad y especificidad, comparándolo contra el patrón de oro (diagnóstico hecho por los Oftalmólogos), para establecer la presencia de retinopatía hipertensiva y/o diabética. | spa |
| dc.description.abstract | The prevalence of diabetes mellitus and hypertension is increasing, making it important to determine the characteristics and diagnostic methods for evaluating patients with diabetic retinopathy (DR) and hypertensive retinopathy (HR) to prevent disease progression. Methods: A cross-sectional study was conducted to identify the epidemiological and clinical characteristics of patients with hypertensive and/or diabetic retinopathy seen in retina clinics at ophthalmological centers in the Colombian Caribbean between October 2017 and October 2019. The study population consisted of 219 patients. The diagnostic performance of two artificial intelligence (AI) models was evaluated by calculating diagnostic accuracy, sensitivity, and specificity, comparing them against the gold standard (diagnosis by ophthalmologists) to establish the presence of hypertensive and/or diabetic retinopathy. | eng |
| dc.format.mimetype | ||
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/17341 | |
| dc.language.iso | spa | |
| dc.publisher | Ediciones Universidad Simón Bolívar | spa |
| dc.publisher | Facultad de Ciencias de la Salud | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | eng |
| dc.rights.accessrights | info:eu-repo/semantics/embargoedAccess | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Retinopatía diabética | spa |
| dc.subject | Retinopatía hipertensiva | spa |
| dc.subject | Fondo de ojo | spa |
| dc.subject | Inteligencia artificial | spa |
| dc.subject.keywords | Diabetic retinopathy | eng |
| dc.subject.keywords | Hypertensive retinopathy | eng |
| dc.subject.keywords | Fundus examination | eng |
| dc.subject.keywords | Artificial intelligence | eng |
| dc.title | Perfil de riesgo cardiovascular y rendimiento de modelos de inteligencia artificial para la detección de retinopatía diabética e hipertensiva en el Caribe Colombiano: un estudio transversal | spa |
| dc.type.driver | info:eu-repo/semantics/other | |
| dc.type.spa | Otros | |
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| dcterms.references | Wolf RM, Heisler M, Smith J, Johnson T, Patel H, Li M, et al. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: The ACCESS randomized trial. Nat Commun. 2024;15:2291. doi:10.1038/s41467-024-46677-5. | eng |
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| dcterms.references | Modjtahedi BS, Wu J, Luong TQ, Gandhi NK, Fong DS, Chen W. Severity of diabetic retinopathy and the risk of future cerebrovascular disease, cardiovascular disease, and all-cause mortality. Ophthalmology. 2021;128(8):1169–79. doi:10.1016/j.ophtha.2020.12.019. | eng |
| dcterms.references | Cheung N, Wong TY. Diabetic retinopathy and systemic vascular complications. J Am Coll Cardiol. 2021;77(12):1451–64. doi:10.1016/j.jacc.2021.01.041. | eng |
| dcterms.references | Yau JWY, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556–64. doi:10.2337/dc11-1909. | eng |
| dcterms.references | Flaxel CJ, Adelman RA, Bailey ST, Fawzi A, Lim JI, Vemulakonda GA, et al. Diabetic retinopathy preferred practice pattern®. Ophthalmology. 2020;127(1):P66–145. doi:10.1016/j.ophtha.2019.09.025. | eng |
| dcterms.references | Teo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045. Ophthalmology. 2021;128(11):1580–91. doi:10.1016/j.ophtha.2021.04.027. | eng |
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| dcterms.references | Hou X, Wang L, Zhu D, Guo L, Weng J, Zhang M, et al. Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China. Nat Commun. 2023;14:4296. doi:10.1038/s41467-023-40049-9. | eng |
| dcterms.references | Medina-Ramírez SA, Soriano-Moreno DR, Tuco KG, Castro-Díaz SD, Alvarado-Villacorta R, Pacheco-Mendoza J, et al. Prevalence and incidence of diabetic retinopathy in Latin America. Int J Ophthalmol. 2022;15(5):765– 78. doi:10.18240/ijo.2022.05.14. | eng |
| dcterms.references | Yau JWY, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2019;42(3):522–9. doi:10.2337/dc18-1180. | eng |
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| dcterms.references | Wong TY, Mitchell P. Hypertensive retinopathy. Hypertension. 2017;69(2):137–44. doi:10.1161/HYPERTENSIONAHA.116.08146. | eng |
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| dcterms.references | Grzybowski A, Brona P, Lim G, Ting DSW. Artificial intelligence for diabetic retinopathy screening. Ophthalmol Ther. 2023;12(1):23–40. doi:10.1007/s40123-022-00573-6. | eng |
| dcterms.references | Chia MA, Landers J, Tapp RJ, Harper CA, Fenwick EK. Detection of diabetic retinopathy using deep learning. Br J Ophthalmol. 2024;108(1):113–20. doi:10.1136/bjo-2023-323105. | eng |
| dcterms.references | Wolf RM, Heisler M, Smith J, Johnson T, Patel H, Li M, et al. Autonomous artificial intelligence increases screening in youth: The ACCESS trial. Nat Commun. 2024;15:2291. doi:10.1038/s41467-024-46677-5. | eng |
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| dcterms.references | Bhimavarapu U, Singh A, Gupta A, Rao A. Artificial intelligence for hypertensive retinopathy using convolutional neural network–support vector machine. Biosensors. 2024;14(1):56. doi:10.3390/bios14010056. | eng |
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| dcterms.references | Wang J, Xie X, Zhang Y, Li Y, Zhao L, Chen Y, et al. Effect of intensive blood pressure–lowering treatment on retinal microvasculature. J Am Coll Cardiol. 2025;85(4):412–23. doi:10.1016/j.jacc.2024.11.032. | eng |
| dcterms.references | Cheung N, Wong TY. Diabetic retinopathy and systemic vascular complications. J Am Coll Cardiol. 2021;77(12):1451–64. doi:10.1016/j.jacc.2021.01.041. | eng |
| oaire.version | info:eu-repo/semantics/acceptedVersion | |
| sb.programa | Especialización en Cardiología | spa |
| sb.sede | Sede Barranquilla | spa |
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