Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía

ilustraciones, fotografías

Autores:: Gallego Suárez, Laura Juliana

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía
dc.title.translated.eng.fl_str_mv	Biomarkers of retinal vein occlusions using a deep learning strategy applied to images obtained by OCT angiography.
title	Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía
spellingShingle	Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía medicina Biochemical markers Eye diseases Marcadores bioquímicos Enfermedades de los ojos Inteligencia Artificial Oclusión Venosa Tomografia Coherencia Optica
title_short	Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía
title_full	Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía
title_fullStr	Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía
title_full_unstemmed	Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía
title_sort	Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía
dc.creator.fl_str_mv	Gallego Suárez, Laura Juliana
dc.contributor.advisor.none.fl_str_mv	Quijano Nieto, Bernardo Alfonso
dc.contributor.author.none.fl_str_mv	Gallego Suárez, Laura Juliana
dc.contributor.educationalvalidator.none.fl_str_mv	Perdomo Charry Oscar Julian
dc.contributor.orcid.spa.fl_str_mv	0000-0001-5056-5956
dc.subject.ddc.spa.fl_str_mv	medicina
topic	medicina Biochemical markers Eye diseases Marcadores bioquímicos Enfermedades de los ojos Inteligencia Artificial Oclusión Venosa Tomografia Coherencia Optica
dc.subject.lemb.eng.fl_str_mv	Biochemical markers Eye diseases
dc.subject.lemb.spa.fl_str_mv	Marcadores bioquímicos Enfermedades de los ojos
dc.subject.proposal.spa.fl_str_mv	Inteligencia Artificial Oclusión Venosa Tomografia Coherencia Optica
description	ilustraciones, fotografías
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-02-07T19:37:18Z
dc.date.available.none.fl_str_mv	2023-02-07T19:37:18Z
dc.date.issued.none.fl_str_mv	2023-02
dc.type.spa.fl_str_mv	Trabajo de grado - Especialidad Médica
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/83367
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/83367 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Rogers S, McIntosh RL, Cheung N, Lim L, Wang JJ, Mitchell P, et al. The Prevalence of Retinal Vein Occlusion: Pooled Data from Population Studies from the United States, Europe, Asia, and Australia. Ophthalmology. 2010 Feb;117(2):313-319.e1. Yasuda M, Kiyohara Y, Arakawa S, Hata Y, Yonemoto K, Doi Y, et al. Prevalence and Systemic Risk Factors for Retinal Vein Occlusion in a General Japanese Population: The Hisayama Study. Investigative Opthalmology & Visual Science. 2010 Jun 1;51(6):3205. Cugati S. Ten-Year Incidence of Retinal Vein Occlusion in an Older Population. Archives of Ophthalmology. 2006 May 1;124(5):726. Klein R. The 15-Year Cumulative Incidence of Retinal Vein Occlusion. Archives of Ophthalmology. 2008 Apr 1;126(4):513. Buehl W, Sacu S, Schmidt-Erfurth U. Retinal Vein Occlusions. In 2010. p. 54–72. Brown G, Yoo J, Brown M, Turpcu A, Rajput Y, Benson W, et al. The Burden of Retinal Venous Occlusion: An Assessment of Fellow Eyes in 1000 Cases. Ophthalmol Retina. 2017 Sep;1(5):404–12. Chang A. The role of artificial intelligence in digital health. Digital health entrepreneurship. 2020;71–81. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan 7;25(1):44–56. Rispoli M, Savastano MC, Lumbroso B. Capillary network anomalies in branch retinal vein occlusion on optical coherence tomography angiography. Retina. 2015 Nov;35(11):2332–8. Simon J, Conliffe T, Kitei P. Non-operative management: An evidence-based approach. In: Seminars in Spine Surgery. Elsevier; 2016. p. 8–13. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr;69:S36–40. Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Surv Ophthalmol. 2019 Mar;64(2):233–40 Tsai G, Banaee T, Conti F, Singh R. Optical coherence tomography angiography in eyes with retinal vein occlusion. J Ophthalmic Vis Res. 2018;13(3):315. Glanville J, Patterson J, McCool R, Ferreira A, Gairy K, Pearce I. Efficacy and safety of widely used treatments for macular oedema secondary to retinal vein occlusion: a systematic review. BMC Ophthalmol. 2014 Dec 21;14(1):7. Hayreh SS, Podhajsky PA, Zimmerman MB. Natural History of Visual Outcome in Central Retinal Vein Occlusion. Ophthalmology. 2011 Jan;118(1):119-133.e2. Hayreh SS, Klugman MR, Beri M, Kimura AE, Podhajsky P. Differentiation of ischemic from non-ischemic central retinal vein occlusion during the early acute phase. Graefe’s Archive for Clinical and Experimental Ophthalmology. 1990;228(3):201–17. Patel A, Nguyen C, Lu S. Central retinal vein occlusion: A review of current Evidence-based treatment options. Middle East Afr J Ophthalmol. 2016;23(1):44. Bowers dk, finkelstein d, wolff sm, green wr. Branch retinal vein occlusion. Retina. 1987;7(4):252–9. Newman-Casey PA, Stem M, Talwar N, Musch DC, Besirli CG, Stein JD. Risk Factors Associated with Developing Branch Retinal Vein Occlusion Among Enrollees in a United States Managed Care Plan. Ophthalmology. 2014 Oct;121(10):1939–48. Jaulim A, Ahmed B, Khanam T, Chatziralli IP. Branch retinal vein occlusion: epidemiology, pathogenesis, risk factors, clinical features, diagnosis, and complications. An update of the literature. Retina. 2013;33(5):901–10. Ho JD, Tsai CY, Liou SW, Tsai RJF, Lin HC. Seasonal Variations in the Occurrence of Retinal Vein Occlusion: A Five-Year Nationwide Population-Based Study from Taiwan. Am J Ophthalmol. 2008 Apr;145(4):722-728.e3. Oh J, Ahn J. Comparison of Retinal Layer Thickness and Vascular Density between Acute and Chronic Branch Retinal Vein Occlusion. Korean Journal of Ophthalmology. 2019;33(3):238. Zawadzki RJ, Capps AG, Dae Yu Kim, Panorgias A, Stevenson SB, Hamann B, et al. Progress on Developing Adaptive Optics–Optical Coherence Tomography for In Vivo Retinal Imaging: Monitoring and Correction of Eye Motion Artifacts. IEEE Journal of Selected Topics in Quantum Electronics. 2014 Mar;20(2):322–33. Coscas F, Glacet-Bernard A, Miere A, Caillaux V, Uzzan J, Lupidi M, et al. Optical Coherence Tomography Angiography in Retinal Vein Occlusion: Evaluation of Superficial and Deep Capillary Plexa. Am J Ophthalmol. 2016 Jan;161:160- 171.e2. Adhi M, Filho MAB, Louzada RN, Kuehlewein L, de Carlo TE, Baumal CR, et al. Retinal Capillary Network and Foveal Avascular Zone in Eyes with Vein Occlusion and Fellow Eyes Analyzed With Optical Coherence Tomography Angiography. Investigative Opthalmology & Visual Science. 2016 Jul 21;57(9):OCT486. Kashani AH, Lee SY, Moshfeghi A, Durbin MK, Puliafito CA. Optical coherence tomography angiography of retinal venous occlusion. Retina. 2015 Nov;35(11):2323–31. Novais EA, Waheed NK. Optical Coherence Tomography Angiography of Retinal Vein Occlusion. In 2016. p. 132–8. Mastropasqua R, Toto L, di Antonio L, Borrelli E, Senatore A, di Nicola M, et al. Optical coherence tomography angiography microvascular findings in macular edema due to central and branch retinal vein occlusions. Sci Rep. 2017 Jan 18;7(1):40763. Suzuki N, Hirano Y, Yoshida M, Tomiyasu T, Uemura A, Yasukawa T, et al. Microvascular Abnormalities on Optical Coherence Tomography Angiography in Macular Edema Associated With Branch Retinal Vein Occlusion. Am J Ophthalmol. 2016 Jan;161:126-132.e1. Glacet-Bernard A, Sellam A, Coscas F, Coscas G, Souied EH. Optical Coherence Tomography Angiography in Retinal Vein Occlusion Treated with Dexamethasone Implant: A New Test for Follow-Up Evaluation. Eur J Ophthalmol. 2016 Sep 7;26(5):460–8. Suzuki N, Hirano Y, Tomiyasu T, Esaki Y, Uemura A, Yasukawa T, et al. Retinal Hemodynamics Seen on Optical Coherence Tomography Angiography Before and After Treatment of Retinal Vein Occlusion. Investigative Opthalmology & Visual Science. 2016 Oct 25;57(13):5681. Savastano MC, Lumbroso B, Rispoli M. In vivo characterization of retinal vascularization morphology using optical coherence tomography angiography. Retina. 2015 Nov;35(11):2196–203. Kadomoto S, Muraoka Y, Ooto S, Miwa Y, Iida Y, Suzuma K, et al. EVALUATION OF MACULAR ISCHEMIA IN EYES WITH BRANCH RETINAL VEIN OCCLUSION. Retina. 2018 Feb;38(2):272–82. Samara WA, Say EAT, Khoo CTL, Higgins TP, Magrath G, Ferenczy S, et al. Correlation of foveal avascular zone size with foveal morphology in normal eyes using optical coherence tomography angiography. Retina. 2015 Nov;35(11):2188– 95. Casselholmde Salles M, Kvanta A, Amrén U, Epstein D. Optical Coherence Tomography Angiography in Central Retinal Vein Occlusion: Correlation Between the Foveal Avascular Zone and Visual Acuity. Investigative Opthalmology & Visual Science. 2016 Jul 13;57(9):OCT242. Sanal MG, Paul K, Kumar S, Ganguly NK. Artificial Intelligence and Deep Learning: The Future of Medicine and Medical Practice. J Assoc Physicians India. 2019 Apr;67(4):71–3. Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Prog Retin Eye Res. 2018 Nov;67:1–29. Li M, Chen Y, Ji Z, Xie K, Yuan S, Chen Q, et al. Image Projection Network: 3D to 2D Image Segmentation in OCTA Images. IEEE Trans Med Imaging. 2020 Nov;39(11):3343–54. Perdomo Charry OJ, González FA. A systematic review of deep learning methods applied to ocular images. Ciencia e Ingenieria Neogranadina. 2020;30(1):9–26. Schlegl T, Waldstein SM, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM, et al. Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. Ophthalmology. 2018 Apr;125(4):549–58. Roy AG, Conjeti S, Karri SPK, Sheet D, Katouzian A, Wachinger C, et al. ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express. 2017 Aug 1;8(8):3627 Lee CS, Tyring AJ, Wu Y, Xiao S, Rokem AS, DeRuyter NP, et al. Generating retinal flow maps from structural optical coherence tomography with artificial intelligence. Sci Rep. 2019 Apr 5;9(1):5694. Yin XX, Sun L, Fu Y, Lu R, Zhang Y. U-Net-Based Medical Image Segmentation. J Healthc Eng. 2022 Apr 15;2022:1–16. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer; 2015. p. 234–41. Du G, Cao X, Liang J, Chen X, Zhan Y. Medical image segmentation based on u- net: A review. Journal of Imaging Science and Technology. 2020 Siddique N, Paheding S, Elkin CP, Devabhaktuni V. U-net and its variants for medical image segmentation: A review of theory and applications. Ieee Access. 2021;9:82031–57. Jaccard P. The distribution of the flora in the alpine zone.1. New phytologist. 1912 Feb;11(2):37–50. Tan PN. Michael Steinbach und Vipin Kumar. Introduction to data mining. 2006 Liu X, Huang Z, Wang Z, Wen C, Jiang Z, Yu Z, et al. A deep learning based pipeline for optical coherence tomography angiography. J Biophotonics. 2019 Oct;12(10). Nagasato D, Tabuchi H, Masumoto H, Enno H, Ishitobi N, Kameoka M, et al. Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning. PLoS One. 2019 Nov 7;14(11):e0223965. Niki T, Muraoka K, Shimizu K. Distribution of Capillary Nonperfusion in Early-stage Diabetic Retinopathy. Ophthalmology. 1984 Dec;91(12):1431–9. Kraus MF, Liu JJ, Schottenhamml J, Chen CL, Budai A, Branchini L, et al. Quantitative 3D-OCT motion correction with tilt and illumination correction, robust similarity measure and regularization. Biomed Opt Express. 2014 Aug 1;5(8):2591. Zhang M, Hwang TS, Dongye C, Wilson DJ, Huang D, Jia Y. Automated Quantification of Nonperfusion in Three Retinal Plexuses Using Projection- Resolved Optical Coherence Tomography Angiography in Diabetic Retinopathy. Investigative Opthalmology & Visual Science. 2016 Oct 3;57(13):5101. Guo Y, Camino A, Wang J, Huang D, Hwang TS, Jia Y. MEDnet, a neural network for automated detection of avascular area in OCT angiography. Biomed Opt Express. 2018 Nov 1;9(11):5147. Alam M, Le D, Son T, Lim JI, Yao X. AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography. Biomed Opt Express. 2020 Sep 1;11(9):5249. Heisler M, Karst S, Lo J, Mammo Z, Yu T, Warner S, et al. Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography. Transl Vis Sci Technol. 2020 Apr 13;9(2):20. Ren X, Feng W, Ran R, Gao Y, Lin Y, Fu X, et al. Artificial intelligence to distinguish retinal vein occlusion patients using color fundus photographs. Eye. 2022;1–7.
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dc.format.extent.spa.fl_str_mv	xiii, 37 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Medicina - Especialidad en Oftalmología
dc.publisher.faculty.spa.fl_str_mv	Facultad de Medicina
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Quijano Nieto, Bernardo Alfonso8137fdb85aaa121f419c1b92e9ebfb42Gallego Suárez, Laura Juliana9947fc0e56dd8d4320a469e139f8f07cPerdomo Charry Oscar Julian0000-0001-5056-59562023-02-07T19:37:18Z2023-02-07T19:37:18Z2023-02https://repositorio.unal.edu.co/handle/unal/83367Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografíasPropósito: Desarrollar un método computacional basado en Deep Learning (DL) para detectar automáticamente biomarcadores de oclusiones de venas retinianas en imágenes adquiridas por angiografía por tomografía de coherencia óptica (OCT-A) Diseño: Desarrollo de algoritmo para detectar biomarcadores de oclusiones de venas retinianas utilizando datos retrospectivos. (Texto tomado de la fuente)Purpose: To develop a computational method based on Deep Learning (DL) to automatically detect biomarkers of retinal vein occlusions in images acquired by optical coherence tomography angiography (OCT- A) Design: Algorithm development for detect biomarkers of retinal vein occlusions using retrospective data. Participants: Images of the superficial, deep, en face, choriocapillaris and outer retina to choriocapillaris (ORCC) layers obtained from 254 patients attended in an Ophthalmology Clinic were used to train and test an artificial intelligence (AI) model. Methods: The OCT-A scans were manually annotated with four biomarkers (BMs): disruption of the perifoveal capillary plexus, non-perfusion areas (NPAs), vascular tortuosity and cystoid spaces. Segmentation and identification were subsequently provided to build and training the DL model using Deep Convolutional Neural Networks (DNN) Main Outcome Measures: detection rate and jaccard index Results: The detection rate of the model for disruption of the perifoveal capillary plexus, non-perfusion areas (NPAs), vascular tortuosity and cystoid spaces were 93%, 92%, 91% and 84% respectively. The Jaccard index values were 0.85, 0.77, 0.72 and 0.73 respectively Conclusion: The proposed DL model may idEspecialidades MédicasEspecialista en Oftalmologíaxiii, 37 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Medicina - Especialidad en OftalmologíaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede BogotámedicinaBiochemical markersEye diseasesMarcadores bioquímicosEnfermedades de los ojosInteligenciaArtificialOclusiónVenosaTomografiaCoherenciaOpticaBiomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografíaBiomarkers of retinal vein occlusions using a deep learning strategy applied to images obtained by OCT angiography.Trabajo de grado - Especialidad Médicainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRogers S, McIntosh RL, Cheung N, Lim L, Wang JJ, Mitchell P, et al. The Prevalence of Retinal Vein Occlusion: Pooled Data from Population Studies from the United States, Europe, Asia, and Australia. Ophthalmology. 2010 Feb;117(2):313-319.e1.Yasuda M, Kiyohara Y, Arakawa S, Hata Y, Yonemoto K, Doi Y, et al. Prevalence and Systemic Risk Factors for Retinal Vein Occlusion in a General Japanese Population: The Hisayama Study. Investigative Opthalmology & Visual Science. 2010 Jun 1;51(6):3205.Cugati S. Ten-Year Incidence of Retinal Vein Occlusion in an Older Population. Archives of Ophthalmology. 2006 May 1;124(5):726.Klein R. The 15-Year Cumulative Incidence of Retinal Vein Occlusion. Archives of Ophthalmology. 2008 Apr 1;126(4):513.Buehl W, Sacu S, Schmidt-Erfurth U. Retinal Vein Occlusions. In 2010. p. 54–72.Brown G, Yoo J, Brown M, Turpcu A, Rajput Y, Benson W, et al. The Burden of Retinal Venous Occlusion: An Assessment of Fellow Eyes in 1000 Cases. Ophthalmol Retina. 2017 Sep;1(5):404–12.Chang A. The role of artificial intelligence in digital health. Digital health entrepreneurship. 2020;71–81.Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan 7;25(1):44–56.Rispoli M, Savastano MC, Lumbroso B. Capillary network anomalies in branch retinal vein occlusion on optical coherence tomography angiography. Retina. 2015 Nov;35(11):2332–8.Simon J, Conliffe T, Kitei P. Non-operative management: An evidence-based approach. In: Seminars in Spine Surgery. Elsevier; 2016. p. 8–13.Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr;69:S36–40.Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Surv Ophthalmol. 2019 Mar;64(2):233–40Tsai G, Banaee T, Conti F, Singh R. Optical coherence tomography angiography in eyes with retinal vein occlusion. J Ophthalmic Vis Res. 2018;13(3):315.Glanville J, Patterson J, McCool R, Ferreira A, Gairy K, Pearce I. Efficacy and safety of widely used treatments for macular oedema secondary to retinal vein occlusion: a systematic review. BMC Ophthalmol. 2014 Dec 21;14(1):7.Hayreh SS, Podhajsky PA, Zimmerman MB. Natural History of Visual Outcome in Central Retinal Vein Occlusion. Ophthalmology. 2011 Jan;118(1):119-133.e2.Hayreh SS, Klugman MR, Beri M, Kimura AE, Podhajsky P. Differentiation of ischemic from non-ischemic central retinal vein occlusion during the early acute phase. Graefe’s Archive for Clinical and Experimental Ophthalmology. 1990;228(3):201–17.Patel A, Nguyen C, Lu S. Central retinal vein occlusion: A review of current Evidence-based treatment options. Middle East Afr J Ophthalmol. 2016;23(1):44.Bowers dk, finkelstein d, wolff sm, green wr. Branch retinal vein occlusion. Retina. 1987;7(4):252–9.Newman-Casey PA, Stem M, Talwar N, Musch DC, Besirli CG, Stein JD. Risk Factors Associated with Developing Branch Retinal Vein Occlusion Among Enrollees in a United States Managed Care Plan. Ophthalmology. 2014 Oct;121(10):1939–48.Jaulim A, Ahmed B, Khanam T, Chatziralli IP. Branch retinal vein occlusion: epidemiology, pathogenesis, risk factors, clinical features, diagnosis, and complications. An update of the literature. Retina. 2013;33(5):901–10.Ho JD, Tsai CY, Liou SW, Tsai RJF, Lin HC. Seasonal Variations in the Occurrence of Retinal Vein Occlusion: A Five-Year Nationwide Population-Based Study from Taiwan. Am J Ophthalmol. 2008 Apr;145(4):722-728.e3.Oh J, Ahn J. Comparison of Retinal Layer Thickness and Vascular Density between Acute and Chronic Branch Retinal Vein Occlusion. Korean Journal of Ophthalmology. 2019;33(3):238.Zawadzki RJ, Capps AG, Dae Yu Kim, Panorgias A, Stevenson SB, Hamann B, et al. Progress on Developing Adaptive Optics–Optical Coherence Tomography for In Vivo Retinal Imaging: Monitoring and Correction of Eye Motion Artifacts. IEEE Journal of Selected Topics in Quantum Electronics. 2014 Mar;20(2):322–33.Coscas F, Glacet-Bernard A, Miere A, Caillaux V, Uzzan J, Lupidi M, et al. Optical Coherence Tomography Angiography in Retinal Vein Occlusion: Evaluation of Superficial and Deep Capillary Plexa. Am J Ophthalmol. 2016 Jan;161:160- 171.e2.Adhi M, Filho MAB, Louzada RN, Kuehlewein L, de Carlo TE, Baumal CR, et al. Retinal Capillary Network and Foveal Avascular Zone in Eyes with Vein Occlusion and Fellow Eyes Analyzed With Optical Coherence Tomography Angiography. Investigative Opthalmology & Visual Science. 2016 Jul 21;57(9):OCT486.Kashani AH, Lee SY, Moshfeghi A, Durbin MK, Puliafito CA. Optical coherence tomography angiography of retinal venous occlusion. Retina. 2015 Nov;35(11):2323–31.Novais EA, Waheed NK. Optical Coherence Tomography Angiography of Retinal Vein Occlusion. In 2016. p. 132–8.Mastropasqua R, Toto L, di Antonio L, Borrelli E, Senatore A, di Nicola M, et al. Optical coherence tomography angiography microvascular findings in macular edema due to central and branch retinal vein occlusions. Sci Rep. 2017 Jan 18;7(1):40763.Suzuki N, Hirano Y, Yoshida M, Tomiyasu T, Uemura A, Yasukawa T, et al. Microvascular Abnormalities on Optical Coherence Tomography Angiography in Macular Edema Associated With Branch Retinal Vein Occlusion. Am J Ophthalmol. 2016 Jan;161:126-132.e1.Glacet-Bernard A, Sellam A, Coscas F, Coscas G, Souied EH. Optical Coherence Tomography Angiography in Retinal Vein Occlusion Treated with Dexamethasone Implant: A New Test for Follow-Up Evaluation. Eur J Ophthalmol. 2016 Sep 7;26(5):460–8.Suzuki N, Hirano Y, Tomiyasu T, Esaki Y, Uemura A, Yasukawa T, et al. Retinal Hemodynamics Seen on Optical Coherence Tomography Angiography Before and After Treatment of Retinal Vein Occlusion. Investigative Opthalmology & Visual Science. 2016 Oct 25;57(13):5681.Savastano MC, Lumbroso B, Rispoli M. 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Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía

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