Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje
Propósito: Determinar la concordancia entre la interpretación de las fotos a color de polo posterior de un especialista en glaucoma y un algoritmo de aprendizaje no supervisado para determinar el daño del nervio óptico. Metodología: Se realizó un estudio de concordancia diagnóstica entre la interpre...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- spa
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/30989
- Acceso en línea:
- https://doi.org/10.48713/10336_30989
https://repository.urosario.edu.co/handle/10336/30989
- Palabra clave:
- Uso de algoritmos de aprendizaje no supervisados en diagnostico medico
Diagnóstico del daño del nervio óptico
Tecnología médica
Interpretación de fotos a color de polo posterior en detección de daño óptico
Daño del nervio óptico según clasificación de Armaly
Medicina experimental
Use of unsupervised learning algorithms in medical diagnosis
Diagnosis of optic disc damage
Medical technology
Interpretation of back pole color photos in detection of optical disc damage
Optic nerve damage according to Armaly classification
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- License
- Abierto (Texto Completo)
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|
dc.title.spa.fl_str_mv |
Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje |
dc.title.TranslatedTitle.spa.fl_str_mv |
Determination of the concordance of optic nerve damage between a Glaucomatologist and a learning algorithm |
dc.title.alternative.spa.fl_str_mv |
DETERMINATION OF THE CONCORDANCE OF OPTIC NERVE DAMAGE BETWEEN A GLAUCOMATOLOGIST AND A LEARNING ALGORITHM |
title |
Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje |
spellingShingle |
Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje Uso de algoritmos de aprendizaje no supervisados en diagnostico medico Diagnóstico del daño del nervio óptico Tecnología médica Interpretación de fotos a color de polo posterior en detección de daño óptico Daño del nervio óptico según clasificación de Armaly Medicina experimental Use of unsupervised learning algorithms in medical diagnosis Diagnosis of optic disc damage Medical technology Interpretation of back pole color photos in detection of optical disc damage Optic nerve damage according to Armaly classification |
title_short |
Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje |
title_full |
Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje |
title_fullStr |
Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje |
title_full_unstemmed |
Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje |
title_sort |
Determinación de la concordancia del daño del nervio optico entre un Glaucomatologo y un algoritmo de aprendizaje |
dc.contributor.advisor.none.fl_str_mv |
Belalcazar Rey, Sandra Rosenstiehl Colón, Shirley Margarita |
dc.contributor.tutor.none.fl_str_mv |
BELALCAZAR REY, SANDRA ROSENSTIEHL COLON, SHIRLEY |
dc.contributor.none.fl_str_mv |
Perdomo, Oscar J. Ríos, Hernán Andrés |
dc.subject.spa.fl_str_mv |
Uso de algoritmos de aprendizaje no supervisados en diagnostico medico Diagnóstico del daño del nervio óptico Tecnología médica Interpretación de fotos a color de polo posterior en detección de daño óptico Daño del nervio óptico según clasificación de Armaly |
topic |
Uso de algoritmos de aprendizaje no supervisados en diagnostico medico Diagnóstico del daño del nervio óptico Tecnología médica Interpretación de fotos a color de polo posterior en detección de daño óptico Daño del nervio óptico según clasificación de Armaly Medicina experimental Use of unsupervised learning algorithms in medical diagnosis Diagnosis of optic disc damage Medical technology Interpretation of back pole color photos in detection of optical disc damage Optic nerve damage according to Armaly classification |
dc.subject.ddc.spa.fl_str_mv |
Medicina experimental |
dc.subject.keyword.spa.fl_str_mv |
Use of unsupervised learning algorithms in medical diagnosis Diagnosis of optic disc damage Medical technology Interpretation of back pole color photos in detection of optical disc damage Optic nerve damage according to Armaly classification |
description |
Propósito: Determinar la concordancia entre la interpretación de las fotos a color de polo posterior de un especialista en glaucoma y un algoritmo de aprendizaje no supervisado para determinar el daño del nervio óptico. Metodología: Se realizó un estudio de concordancia diagnóstica entre la interpretación de las fotos a color de polo posterior de un especialista en glaucoma y un algoritmo de aprendizaje no supervisado con respecto a la identificación del daño del nervio óptico según el sistema de clasificación de Armaly y usando el coeficiente de kappa de Cohen. Resultados: El algoritmo de aprendizaje no supervisado evaluó 689 fotos a color de polo posterior, clasificadas como con nervio óptico sano (sin daño) y con daño leve, moderado y severo. Posteriormente un clasificador K-means, agrupó las características extraídas en los cuatro grupos mencionados y se obtuvo un coeficiente kappa de Cohen de 0.037. Cuando se clasificaron las imágenes en dos grupos, sanos y con daño, se evidenció un estadístico kappa para la clasificación dicotómica de 0.03. Conclusión: El Algoritmo de aprendizaje no supervisado usado para la clasificación de daño del nervio óptico en fotos a color de polo posterior, mostró una mala concordancia con la realizada por el especialista en glaucoma según el sistema de clasificación de Armaly. |
publishDate |
2021 |
dc.date.accessioned.none.fl_str_mv |
2021-02-26T17:49:24Z |
dc.date.available.none.fl_str_mv |
2021-02-26T17:49:24Z |
dc.date.created.none.fl_str_mv |
2021-02-09 |
dc.type.eng.fl_str_mv |
bachelorThesis |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
dc.type.document.spa.fl_str_mv |
Revisión sistemática |
dc.type.spa.spa.fl_str_mv |
Trabajo de grado |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.48713/10336_30989 |
dc.identifier.uri.none.fl_str_mv |
https://repository.urosario.edu.co/handle/10336/30989 |
url |
https://doi.org/10.48713/10336_30989 https://repository.urosario.edu.co/handle/10336/30989 |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.acceso.spa.fl_str_mv |
Abierto (Texto Completo) |
rights_invalid_str_mv |
Abierto (Texto Completo) http://purl.org/coar/access_right/c_abf2 |
dc.format.extent.spa.fl_str_mv |
29 |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Universidad del Rosario |
dc.publisher.department.spa.fl_str_mv |
Escuela de Medicina y Ciencias de la Salud |
dc.publisher.program.spa.fl_str_mv |
Especialización en Oftalmología |
institution |
Universidad del Rosario |
dc.source.bibliographicCitation.spa.fl_str_mv |
Shaikh Y, Yu F, Coleman AL. Burden of undetected and untreated glaucoma in the United States. Am J Ophthalmol [Internet]. 2014;158(6):1121-1129.e1. Available from: http://dx.doi.org/10.1016/j.ajo.2014.08.023 Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology [Internet]. 2014;121(11):2081–90. Available from: http://dx.doi.org/10.1016/j.ophtha.2014.05.013 Henderer JD. Disc damage likelihood scale. Br J Ophthalmol. 2006;90(4):395–6. Shickle D, Todkill D, Chisholm C, Rughani S, Griffin M, Cassels-Brown A, et al. Addressing inequalities in eye health with subsidies and increased fees for General Ophthalmic Services in socio-economically deprived communities: A sensitivity analysis. Public Health. 2015;129(2):131–7 Hattenhauer MG, Johnson DH, Ing HH, Herman DC, Hodge DO, Yawn BP, et al. The probability of blindness from open-angle glaucoma. Ophthalmology. 1998;105(11):2099–104 Stevens GA, White RA, Flaxman SR, Price H, Jonas JB, Keeffe J, et al. Global prevalence of vision impairment and blindness: Magnitude and temporal trends, 1990-2010. Ophthalmology. 2013;120(12):2377–84. Varma R, Ying-Lai M, Francis BA, Nguyen BBT, Deneen J, Wilson MR, et al. Prevalence of open-angle glaucoma and ocular hypertension in Latinos: The Los Angeles Latino Eye Study. Ophthalmology. 2004;111(8):1439–48. Quigley HA, West SK, Rodriguez J, Munoz B, Klein R, Snyder R. The prevalence of glaucoma in a population-based study of Hispanic subjects: Proyecto VER. Arch Ophthalmol. 2001;119(12):1819–26. Caprioli J. Clinical evaluation of the optic nerve in glaucoma. Trans Am Ophthalmol Soc. 1994;92:589–641. Gordon MO, Torri V, Miglior S, Beiser JA, Floriani I, Miller JP, et al. Validated prediction model for the development of primary open-angle glaucoma in individuals with ocular hypertension. Ophthalmology. 2007;114(1):10-19.e2. Myers JS, Fudemberg S LD. Evolution of optic nerve photography for glaucoma screening: a review. Clin Exp Ophthalmol. 2018;13(3):287–8. Chakrabarty L, Joshi GD, Chakravarty A, Raman G V., Krishnadas SR, Sivaswamy J. Automated Detection of Glaucoma from Topographic Features of the Optic Nerve Head in Color Fundus Photographs. J Glaucoma. 2016;25(7):590–7. Cook C, Foster P. Epidemiology of glaucoma: What’s new? Can J Ophthalmol. 2012;47(3):223–6. Bock R, Meier J, Nyúl LG, Hornegger J, Michelson G. Glaucoma risk index: Automated glaucoma detection from color fundus images. Med Image Anal. 2010;14(3):471–81. Spaeth GL, Reddy SC. Imaging of the optic disk in caring for patients with glaucoma: Ophthalmoscopy and photography remain the gold standard. Surv Ophthalmol [Internet]. 2014;59(4):454–8. Available from: http://dx.doi.org/10.1016/j.survophthal.2013.10.004 Hasanreisoglu M, Priel E, Naveh L, Lusky M, Weinberger D, Benjamini Y, et al. Screening for glaucoma with stereo disc photopgraphy. J Glaucoma. 1995;22(3):238–42. Vijaya L, George R, Paul PG, Baskaran M, Arvind H, Raju P, et al. Prevalence of open-angle glaucoma in a rural south Indian population. Investig Ophthalmol Vis Sci. 2005;46(12):4461–7. Ramakrishnan R, Nirmalan PK, Krishnadas R, Thulasiraj RD, Tielsch JM, Katz J, et al. Glaucoma in a rural population of Southern India: The Aravind Comprehensive Eye Survey. Ophthalmology. 2003;110(8):1484–90. Haleem MS, Han L, van Hemert J, Li B. Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review. Comput Med Imaging Graph [Internet]. 2013;37(7–8):581–96. Available from: http://dx.doi.org/10.1016/j.compmedimag.2013.09.005 Rahimy E. Deep learning applications in ophthalmology. Curr Opin Ophthalmol. 2018;29(3):254–60. Lee A, Taylor P, Kalpathy-Cramer J. Machine Learning Has Arrived! Ophthalmology. 2017;124(12):1726–8. Kotowski J, Wollstein G IH. Imaging of the optic nerve and retinal nerve fiber layer: an essential part of glaucoma diagnosis and monitoring. Surv Ophthalmol. 2014;23(1):1–7. Quigley H, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006;90(3):262–7. Bourne RRA, Taylor HR, Flaxman SR, Keeffe J, Leasher J, Naidoo K, et al. Number of people blind or visually impaired by glaucoma worldwide and in world regions 1990 - 2010: A meta-analysis. PLoS One. 2016;11(10):1–16. Foster PJ, Buhrmann R, Quigley HA, Johnson GJ. The definition and classification of glaucoma in prevalence surveys. Br J Ophthalmol. 2002;86(2):238–42. Caprioli J, Prum B, Zeyen T. Comparison of methods to evaluate the optic nerve head and nerve fiber layer for glaucomatous change. Am J Ophthalmol. 1996;121(6):659–67. Liu JHK, Zhang X, Kripke DF, Weinreb RN. Twenty-four-hour intraocular pressure pattern associated with early glaucomatous changes. Investig Ophthalmol Vis Sci. 2003;44(4):1586–90. Leske MC. Distribution of Intraocular Pressure. Arch Ophthalmol. 1997;115(8):1051. Wall M, Stanek KE, Chauhan BC. Variability in patients with glaucomatous visual field damage is reduced using size V stimuli. Investig Ophthalmol Vis Sci. 1996;37(3). Reus NJ, Lemij HG, Garway-Heath DF, Airaksinen PJ, Anton A, Bron AM, et al. Clinical Assessment of Stereoscopic Optic Disc Photographs for Glaucoma: The European Optic Disc Assessment Trial. Ophthalmology [Internet]. 2010;117(4):717–23. Available from: http://dx.doi.org/10.1016/j.ophtha.2009.09.026 Ervin A, Boland M, Myrowitz E, Prince J, Hawkins B, Vollenweider D, et al. Treatment for Glaucoma: Comparative Effectiveness. Comparative Effectiveness Review. AHRQ Publ No 12-EHC038-EF [Internet]. 2012;Review No.(60):443. Available from: www.effectivehealthcare.ahrq.gov/reports/final.cfm.%5Cnwww.effectivehealthcare.ahrq.gov/reports/final.cfm Lee CS, Baughman DM, Lee AY. Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images. Kidney Int Reports [Internet]. 2017;1(4):322–7. Available from: http://dx.doi.org/10.1016/j.oret.2016.12.009 Gargeya R, Leng T. Automated Identification of Diabetic Retinopathy Using Deep Learning. Ophthalmology [Internet]. 2017;124(7):962–9. Available from: http://dx.doi.org/10.1016/j.ophtha.2017.02.008 Christopher M, Belghith A, Bowd C, Proudfoot JA, Goldbaum MH, Weinreb RN, et al. Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. Sci Rep. 2018;8(1):1–13. Medeiros FA, Jammal AA, Thompson AC. From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs. Ophthalmology [Internet]. 2019;126(4):513–21. Available from: https://doi.org/10.1016/j.ophtha.2018.12.033 Simonyan K ZA. Very Deep Convolutional Networks for Large-Scale Image Recognition. Int Conf Learn Represent. 2015;75(6):398–406. Sivaswamy J, Gopal SRK, Joshi D, Jain M, Syed U, Hospital AE. DRISHTI-GS : Retinal image dataset for optic nerve head segmentation. 2014;53–6. Fumero F, Alayon S, Sanchez JL, Sigut J, Gonzalez-Hernandez M. RIM-ONE: An open retinal image database for optic nerve evaluation. Proc - IEEE Symp Comput Med Syst. 2011;2–7. Association WM. WorldMedical Association Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects. JAMA - J Am Med Assoc. 2013;310. Colombia R de. Ministerio de Salud. Resolucion Numero 8430 de 1993. RN 008430. 2012;32(4):471–3. Ting DSW, Cheung CYL, 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 - J Am Med Assoc. 2017;318(22):2211–23. Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology [Internet]. 2018;125(8):1199–206. Available from: https://doi.org/10.1016/j.ophtha.2018.01.023 Phene S, Dunn RC, Hammel N, Liu Y, Krause J, Kitade N, et al. Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs. Ophthalmology [Internet]. 2019;126(12):1627–39. Available from: https://doi.org/10.1016/j.ophtha.2019.07.024 Christopher M, Belghith A, Weinreb RN, Bowd C, Goldbaum MH, Saunders LJ, et al. Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict glaucoma progression. Investig Ophthalmol Vis Sci. 2018;59(7):2748–56. Goldbaum MH, Sample PA, Zhang Z, Chan K, Lee T, Boden C, et al. Using Unsupervised Learning with Independent Component Analysis to Identify Patterns of Glaucomatous Visual Field Defects. Invest Ophthalmol. 2005;46(10):3676–83. |
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Perdomo, Oscar J.Ríos, Hernán AndrésBelalcazar Rey, Sandra0a7a696c-31e3-4dc7-a802-f0556fa17a28600Rosenstiehl Colón, Shirley Margarita6d8d9e9f-0b06-47ac-85ef-126b45e9cc82600BELALCAZAR REY, SANDRAROSENSTIEHL COLON, SHIRLEYCarpio Rosso Delgado, VanessaEspecialista en OftalmologíaFull timeb2b636ca-77a1-4483-abf6-fa0cab7452426002021-02-26T17:49:24Z2021-02-26T17:49:24Z2021-02-09Propósito: Determinar la concordancia entre la interpretación de las fotos a color de polo posterior de un especialista en glaucoma y un algoritmo de aprendizaje no supervisado para determinar el daño del nervio óptico. Metodología: Se realizó un estudio de concordancia diagnóstica entre la interpretación de las fotos a color de polo posterior de un especialista en glaucoma y un algoritmo de aprendizaje no supervisado con respecto a la identificación del daño del nervio óptico según el sistema de clasificación de Armaly y usando el coeficiente de kappa de Cohen. Resultados: El algoritmo de aprendizaje no supervisado evaluó 689 fotos a color de polo posterior, clasificadas como con nervio óptico sano (sin daño) y con daño leve, moderado y severo. Posteriormente un clasificador K-means, agrupó las características extraídas en los cuatro grupos mencionados y se obtuvo un coeficiente kappa de Cohen de 0.037. Cuando se clasificaron las imágenes en dos grupos, sanos y con daño, se evidenció un estadístico kappa para la clasificación dicotómica de 0.03. Conclusión: El Algoritmo de aprendizaje no supervisado usado para la clasificación de daño del nervio óptico en fotos a color de polo posterior, mostró una mala concordancia con la realizada por el especialista en glaucoma según el sistema de clasificación de Armaly.Purpose: To determine the concordance between an Unsupervised Learning Algorithm and eye fundus color photos interpretation by a specialist for the identification of the optic disc damage. Methodology: A concordance study between an Unsupervised Learning Algorithm and a glaucoma specialist was made. The Cohen's kappa coefficient was calculated for identification of the optic disc damage in eye fundus color photos and were assessed according to Armaly´s cup/disc ratio classification. Results: The Unsupervised Learning Algorithm evaluated 689 color optic disc images classified as: healthy (no damage), mild, moderate and severe damage. A k-means classifier clustered the extracted features in four groups and obtained a Cohen's kappa coefficient of 0.037 While classifying the images in two groups: Healthy and with damage, we found a Cohen's kappa coefficient of 0.03. Conclusion: The Unsupervised Learning Algorithm for the classification of optic disc damage on color fundus photos showed a bad concordance with the one done by the glaucoma specialist, using Armaly`s cup/disc ratio classification.29application/pdfhttps://doi.org/10.48713/10336_30989https://repository.urosario.edu.co/handle/10336/30989spaUniversidad del RosarioEscuela de Medicina y Ciencias de la SaludEspecialización en OftalmologíaAbierto (Texto Completo)EL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma. PARGRAFO: En caso de presentarse cualquier reclamación o acción por parte de un tercero en cuanto a los derechos de autor sobre la obra en cuestión, EL AUTOR, asumirá toda la responsabilidad, y saldrá en defensa de los derechos aquí autorizados; para todos los efectos la universidad actúa como un tercero de buena fe. EL AUTOR, autoriza a LA UNIVERSIDAD DEL ROSARIO, para que en los términos establecidos en la Ley 23 de 1982, Ley 44 de 1993, Decisión andina 351 de 1993, Decreto 460 de 1995 y demás normas generales sobre la materia, utilice y use la obra objeto de la presente autorización. -------------------------------------- POLITICA DE TRATAMIENTO DE DATOS PERSONALES. Declaro que autorizo previa y de forma informada el tratamiento de mis datos personales por parte de LA UNIVERSIDAD DEL ROSARIO para fines académicos y en aplicación de convenios con terceros o servicios conexos con actividades propias de la academia, con estricto cumplimiento de los principios de ley. Para el correcto ejercicio de mi derecho de habeas data cuento con la cuenta de correo habeasdata@urosario.edu.co, donde previa identificación podré solicitar la consulta, corrección y supresión de mis datos.http://purl.org/coar/access_right/c_abf2Shaikh Y, Yu F, Coleman AL. Burden of undetected and untreated glaucoma in the United States. Am J Ophthalmol [Internet]. 2014;158(6):1121-1129.e1. Available from: http://dx.doi.org/10.1016/j.ajo.2014.08.023Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology [Internet]. 2014;121(11):2081–90. Available from: http://dx.doi.org/10.1016/j.ophtha.2014.05.013Henderer JD. Disc damage likelihood scale. Br J Ophthalmol. 2006;90(4):395–6.Shickle D, Todkill D, Chisholm C, Rughani S, Griffin M, Cassels-Brown A, et al. Addressing inequalities in eye health with subsidies and increased fees for General Ophthalmic Services in socio-economically deprived communities: A sensitivity analysis. Public Health. 2015;129(2):131–7Hattenhauer MG, Johnson DH, Ing HH, Herman DC, Hodge DO, Yawn BP, et al. The probability of blindness from open-angle glaucoma. Ophthalmology. 1998;105(11):2099–104Stevens GA, White RA, Flaxman SR, Price H, Jonas JB, Keeffe J, et al. Global prevalence of vision impairment and blindness: Magnitude and temporal trends, 1990-2010. Ophthalmology. 2013;120(12):2377–84.Varma R, Ying-Lai M, Francis BA, Nguyen BBT, Deneen J, Wilson MR, et al. Prevalence of open-angle glaucoma and ocular hypertension in Latinos: The Los Angeles Latino Eye Study. Ophthalmology. 2004;111(8):1439–48.Quigley HA, West SK, Rodriguez J, Munoz B, Klein R, Snyder R. The prevalence of glaucoma in a population-based study of Hispanic subjects: Proyecto VER. Arch Ophthalmol. 2001;119(12):1819–26.Caprioli J. Clinical evaluation of the optic nerve in glaucoma. Trans Am Ophthalmol Soc. 1994;92:589–641.Gordon MO, Torri V, Miglior S, Beiser JA, Floriani I, Miller JP, et al. Validated prediction model for the development of primary open-angle glaucoma in individuals with ocular hypertension. Ophthalmology. 2007;114(1):10-19.e2.Myers JS, Fudemberg S LD. Evolution of optic nerve photography for glaucoma screening: a review. Clin Exp Ophthalmol. 2018;13(3):287–8.Chakrabarty L, Joshi GD, Chakravarty A, Raman G V., Krishnadas SR, Sivaswamy J. Automated Detection of Glaucoma from Topographic Features of the Optic Nerve Head in Color Fundus Photographs. J Glaucoma. 2016;25(7):590–7.Cook C, Foster P. Epidemiology of glaucoma: What’s new? Can J Ophthalmol. 2012;47(3):223–6.Bock R, Meier J, Nyúl LG, Hornegger J, Michelson G. Glaucoma risk index: Automated glaucoma detection from color fundus images. Med Image Anal. 2010;14(3):471–81.Spaeth GL, Reddy SC. Imaging of the optic disk in caring for patients with glaucoma: Ophthalmoscopy and photography remain the gold standard. Surv Ophthalmol [Internet]. 2014;59(4):454–8. Available from: http://dx.doi.org/10.1016/j.survophthal.2013.10.004Hasanreisoglu M, Priel E, Naveh L, Lusky M, Weinberger D, Benjamini Y, et al. Screening for glaucoma with stereo disc photopgraphy. J Glaucoma. 1995;22(3):238–42.Vijaya L, George R, Paul PG, Baskaran M, Arvind H, Raju P, et al. Prevalence of open-angle glaucoma in a rural south Indian population. Investig Ophthalmol Vis Sci. 2005;46(12):4461–7.Ramakrishnan R, Nirmalan PK, Krishnadas R, Thulasiraj RD, Tielsch JM, Katz J, et al. Glaucoma in a rural population of Southern India: The Aravind Comprehensive Eye Survey. Ophthalmology. 2003;110(8):1484–90.Haleem MS, Han L, van Hemert J, Li B. Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review. Comput Med Imaging Graph [Internet]. 2013;37(7–8):581–96. 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