Rev Med UAS
Vol. 13: No. 1. Enero-Marzo 2023
ISSN 2007-8013

Diagnóstico y clasificación de la retinopatía diabética utilizando imágenes de fondo de ojo de campo ultra amplio, comparando los sistemas Optos® y Clarus 700®

Diagnosis and classification of diabetic retinopathy using ultra-wide field fundus imaging, comparing Optos® and Clarus 700® systemsrs

Karen Analí García-Medina1,2*, Efraín Romo-García1,2

  1. Hospital Civil de Culiacán/Centro de Investigación y Docencia en Ciencias de la Salud, Servicio de Oftalmología, departamento de Retina y vítreo, Culiacán, Sinaloa, México.
  2. Hospital Oftalmológico de Sinaloa, departamento de Retina y vítreo, Culiacán, Sinaloa, México.

* Correspondencia: Karen Analí García-Medina
Departamento de Retina y vítreo, Hospital Civil de Culiacán/Centro de Investigación y Docencia en Ciencias de la Salud, Culiacán, Sinaloa, México.
Prolongación Álvaro Obregón 1422, Tierra Blanca, 80030 Culiacán Rosales, Sinaloa, México

DOI http://dx.doi.org/10.28960/revmeduas.2007-8013.v13.n1.005

Texto Completo PDF

Recibido 2 de marzo 2023, aceptado 29 de abril 2023

RESUMEN
Objetivo: determinar la concordancia en el diagnóstico y clasificación de la retinopatía diabética utilizando imágenes de fondo de ojo de campo ultra amplio, comparando los sistemas Optos® y Clarus 700®. Materiales y métodos: se realizó un estudio comparativo, descriptivo, prospectivo y transversal en el que se incluyeron 144 ojos de 77 pacientes (41 hombres y 36 mujeres) para con una confianza del 95%, estimar el coeficiente de concordancia K (kappa). --- Resultados: el coeficiente Kappa de Cohen obtenido fue de .846, que se traduce como una concordancia muy buena entre los sistemas Optos® y Clarus 700® en el diagnóstico y clasificación de la retinopatía diabética utilizando imágenes de fondo de ojo de campo ultra amplio. --- Conclusiones: ambos sistemas de imagen de fondo de ojo de campo ultra-amplio mostraron ser similares en el diagnóstico y clasificación de la retinopatía diabética; sin embargo, Optos® permitió imágenes de fondo de ojo más amplias que Clarus 700®; mientras que Clarus 700® produjo menos artefactos y proporcionó imágenes más detalladas del fondo de ojo. No se tiene registro de estudios previos que comparen ambos sistemas de campo ultra amplio que se hayan realizado en México, lo cual permite utilizar la información obtenida como base para estudios posteriores.
Palabras clave. Diabetes mellitus, retinopatía diabética, sistemas de imágenes retinales de campo ultra amplio.

ABSTRACT
Objective: to determine the concordance in the diagnosis and classification of diabetic retinopathy using ultra-wide field fundus images, comparing the Optos® and Clarus 700® systems. --- Materials and methods: a comparative, descriptive, prospective and cross-sectional study was carried out in which 144 eyes of 77 patients (41 men and 36 women) were included to estimate the K (kappa) concordance coefficient with a confidence of 95%. --- Results: Cohen's Kappa coefficient obtained was .846, which translates as very good agreement between the Optos® and Clarus 700® systems in the diagnosis and classification of diabetic retinopathy using ultra-wide field fundus images. --- Conclusions: both ultra-wide field fundus imaging systems proved to be similar in the diagnosis and classification of diabetic retinopathy; however, Optos® allowed for larger fundus images than Clarus 700®; while Clarus 700® produced fewer artifacts and provided more detailed fundus images. There is no record of previous studies that compare both ultra-wide field systems that have been carried out in Mexico, which allows using the information obtained as a basis for subsequent studies.
Keywords. Diabetic mellitus, diabetic retinopathy, Ultra-wide-field retinal imaging systems.

Referencias

  1. Lechner J, O'Leary OE, Stitt AW. The pathology associated with diabetic retinopathy. Vision Res. 2017 Oct 1;139:7-14.
  2. Honasoge A, Nudleman E, Smith M, Rajagopal R. Emerging insights and interventions for diabetic retinopath. Curr Diab Rep. 2019 Oct;19(10):1-6.
  3. Altmann C, Schmidt MH. The role of microglia in diabetic retinopathy: inflammation, microvasculature defects and neurodegeneration. Int J Mol Sci. 2018 Jan;19(1):110.
  4. Kashim RM, Newton P, Ojo O. Diabetic retinopathy screening: A systematic review on patients’ non-attendance. Int J Environ Res Public Health. 2018 Jan;15(1):157.
  5. Cheloni R, Gandolfi SA, Signorelli C, Odone A. Global prevalence of diabetic retinopathy: protocol for a systematic review and meta-analysis. BMJ Open. 2019 Mar 1;9(3):e022188.
  6. Wat N, Wong RL, Wong IY. Associations between diabetic retinopathy and systemic risk factors. Hong Kong Med J. 2016 Dec 1;22(6):589-99.
  7. Simó-Servat O, Hernández C, Simó R. Diabetic retinopathy in the context of patients with diabetes. Ophthalmic Res. 2019;62(4):211-7.
  8. Zheng Y, Lamoureux EL, Lavanya R, Wu R, Ikram MK, Wang JJ et al. Prevalence and risk factors of diabetic retinopathy in migrant Indians in an urbanized society in Asia: the Singapore Indian eye study. Ophthalmology. 2012 Oct 1;119(10):2119-24.
  9. Raman R, Krishnan R, Kim Ramasamy SN. Diabetic retinopathy: A right time to intervene. Indian J Ophthalmol. 2020 Feb;68(2):305.
  10. Wong TY, Sabanayagam C. Strategies to tackle the global burden of diabetic retinopathy: from epidemiology to artificial intelligence. Ophthalmol. 2020;243(1):9-20.
  11. Solomon SD, Goldberg MF. ETDRS grading of diabetic retinopathy: still the gold standard?. Ophthalmic Res. 2019;62(4):190-5.
  12. Bandello F, Cicinelli MV. 19th EURETINA congress keynote lecture: diabetic retinopathy today. Ophthalmologica. 2020;243(3):163-71.
  13. Wang W, Lo AC. Diabetic retinopathy: pathophysiology and treatments. Int J Mol Sci. 2018 Jun;19(6):1816.
  14. Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian J Ophthalmol. 2019 Jul;67(7):1004.
  15. Liu TA, Arevalo JF. Wide-field imaging in proliferative diabetic retinopathy. Int J Retin Vitr. 2019 Dec;5(1):1-4.
  16. Falavarjani KG, Wang K, Khadamy J, Sadda SR. Ultra-wide-field imaging in diabetic retinopathy; an overview. J. Curr Ophthalmol. 2016 Jun 1;28(2):57-60.
  17. Linz MO, Scott AW. Wide-field imaging of sickle retinopathy. Int J Retin Vitr. 2019 Dec;5(1):1-1.
  18. Byberg S, Vistisen D, Diaz L, Charles MH, Hajari JN, Valerius M et al. Optos wide‐field imaging versus conventional camera imaging in Danish patients with type 2 diabetes. Acta Ophthalmol. 2019 Dec;97(8):815-20.
  19. Rabiolo A, Parravano M, Querques L, Cicinelli MV, Carnevali A, Sacconi R et al. Ultra-wide-field fluorescein angiography in diabetic retinopathy: a narrative review. Clin Ophthalmol. 2017;11:803.
  20. Oh K, Kang HM, Leem D, Lee H, Seo KY, Yoon S. Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images. Sci Rep. 2021 Jan 21;11(1):1-9.
  21. Ting DS, Cheung CY, Lim G, Tan GS, 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 Dec 12;318(22):2211-23.
  22. Lim G, Bellemo V, Xie Y, Lee XQ, Yip MY, Ting DS. Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review. Eye Vis. 2020 Dec;7(1):1-3.
  23. Hirano T, Imai A, Kasamatsu H, Kakihara S, Toriyama Y, Murata T. Assessment of diabetic retinopathy using two ultra-wide-field fundus imaging systems, the Clarus® and Optos™ systems. BMC Ophthalmol. 2018 Dec;18(1):1-7.