Retinal fundus image analysis and classification for glaucoma risk assessment

Non-invasive: quantusGL is based on the analysis of a fundus photograph of the retina taken by an ocular retinograph, thus providing the opportunity for retinal fundus photograph taken by an ocular retinograph, thus providing the opportunity to avoid the need of an invasive technique to predict the risk of glaucoma.

Fast: quantusGL generates accurate results in just a few minutes.

Comparison of quantusGL and other commercial glaucoma tests:

  Sensitivity Specificity
Ophthalmoscopy 47.0% 94.0%
Optical disc photograph 73.0% 89.0%
Assessment of the nerve fiber layer by photography 75.0% 88.0%
Heidelberg II retinal tomography 86.0% 89.0%
Tomometer 46.0% 95.0%
quantusGL 84.1% 95.8%

WHY DOES quantusGL work?

An automated support tool is defined as one that requires minimal or no input from the physician to obtain a result. Over the past few years, research has focused on automated algorithms to improve current imaging-based clinical diagnosis. The rise of Arti cial Intelligence techniques, and especially Deep Learning, has increased the number of studies using this type of algorithm in diagnostic ology. Published studies show that glaucoma detection using trained Deep Learning models can achieve high accuracy in diverse populations and provide quantitative comparisons of how the model's performance can vary across data sets consisting of glaucoma of different disease severity and ethnicity.

quantusGL is presented as a novel method of Artificial Intelligence to identify patterns associated with specific pathologies and to determine the risk of glaucoma. According to several studies, the various tests and tools used by oftalmologists give an individual sensitivity of 39-50% (see references 37-41 ), and the combination of several of them is necessary to obtain a more accurate diagnosis. Therefore, quantusGL, which has a sensitivity of 84% (see reference 43), is ideal to assist in the diagnosis of atherosclerosis.

WHEN TO USE quantusGL?

quantusGL has been designed with a clear focus on the general population and aims to be a glaucoma detection tool, being of great help in screening patients with risk factors and prioritizing waiting lists. The possibilities of using the product will be diverse, ranging from a medical office in primary care to the ophthalmology or optometry unit.


  1. Weinreb RN, Aung T, Medeiros FA. The pathophysiology and treatment of glaucoma: A review. JAMA - J Am Med Assoc. 2014;311(18):1901-1911. doi:10.1001/jama.2014.3192
  2. ResearchGate. Accessed July 17, 2020. https://www.researchgate.net/publication/282792352_Glaucoma_A_brief_review/link/56f4d5b708ae95e8b6d06bbb/download
  3. Types of Glaucoma | National Eye Ins tute. Accessed July 27, 2020. https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/glaucoma/types-glaucoma
  4. 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. 2014;121(11):2081-2090. doi:10.1016/j.ophtha.2014.05.013
  5. Kingman S. Glaucoma is the second leading cause of blindness globally. Bull World Health Organ. 2004;82(11):887-888. doi:/S0042-96862004001100019
  6. Five Common Glaucoma Tests | Glaucoma Research Founda on. Accessed July 27, 2020. https://www.glaucoma.org/glaucoma/diagnostic-tests.php
  7. High Eye Pressure and Glaucoma | Glaucoma Research Founda on. Accessed July 27, 2020. https://www.glaucoma.org/gleams/high-eye-pressure-andglaucoma.php
  8. Díaz Pinto AY. Machine Learning for Glaucoma Assessment using Fundus Images. Published online June 26, 2019. doi:10.4995/Thesis/10251/124351
  9. Sharma P, Sample PA, Zangwill LM, Schuman JS. Diagnos c Tools for Glaucoma Detection and Management. Surv Ophthalmol. 2008;53(6 SUPPL.):S17. doi:10.1016/j.survophthal.2008.08.003
  10. Chalakkal RJ, Abdulla WH, Hong SC. Fundus re nal image analyses for screening and diagnosing diabetes care nopathy, macular edema, and glaucoma disorders. In: Diabetes and Fundus OCT. Elsevier; 2020:59-111. doi:10.1016/b978-0-12-817440-1.00003-6
  11. Kumar JRH, Seelamantula CS, Kamath YS, Jampala R. Rim-to-Disc Ra o Outperforms Cup-to-Disc Ra o for Glaucoma Prescreening. Sci Rep. 2019;9(1). doi:10.1038/s41598-019-43385-2
  12. Fernandez-Granero MA, Sarmiento A, Sanchez-Morillo D, Jiménez S, Alemany P, Fondón I. Automatic CDR Estimation for Early Glaucoma Diagnosis. JHealthc Eng. 2017;2017. doi:10.1155/2017/5953621
  13. Das P, Nirmala SR, Medhi JP. Diagnosis of glaucoma using CDR and NRR area in re na images. Netw Model Anal Heal Informa cs Bioinforma. 2016;5(1). doi:10.1007/s13721-015-0110-5
  14. Verdú-Monedero R, Morales-Sánchez J, Berenguer-Vidal R, Sellés-Navarro I, Palazón-Cabanes A. Automatic Measurement of ISNT and CDR on RetinalImages by Means of a Fast and E cient Method Based on Mathema cal Morphology and Active Contours. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinforma cs). Vol 11487 LNCS. Springer Verlag; 2019:361-370. doi:10.1007/978-3- 030-19651-6_35
  15. Wong DWK, Liu J, Lim JH, et al. Level-set based automatic cup-to-disra o determina on using retinal fundus images in argali. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS’08 - “Personalized Healthcare through Technology.” Vol 2008. IEEE Computer Society; 2008:2266-2269. doi:10.1109/iembs.2008.4649648
  16. Joshi GD, Sivaswamy J, Krishnadas SR. Op c disk and cup segmenta on from monocular color re nal images for glaucoma assessment. IEEE Trans Med Imaging. 2011;30(6):1192-1205. doi:10.1109/TMI.2011.2106509
  17. Diaz A, Morales S, Naranjo V, Alcoceryz P, Lanzagortayz A. Glaucoma diagnosis by means of op c cup feature analysis in color fundus images. In:European Signal Processing Conference. Vol 2016-November. European Signal Processing Conference, EUSIPCO; 2016:2055-2059. doi:10.1109/EUSIPCO.2016.7760610
  18. Fu H, Cheng J, Xu Y, Liu J. Glaucoma Detection Based on Deep Learning Network in Fundus Image. In: Advances in Computer Vision and Pattern Recognition.SpringerLondon;2019:119-137.doi:10.1007/978-3-030-13969-8_6
  19. Abbas Q. Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning. Int J Adv Comput Sci Appl. 2017;8(6).
  20. Orlando JI, Prokofyeva E, del Fresno M, Blaschko MB. Convolu onal neural network transfer for automated glaucoma iden fica on. In: Romero E, Lepore N, Brieva J, Larrabide I, eds. 12th International Symposium on Medical Informa on Processing and Analysis. Vol 10160. SPIE; 2017:101600U.doi:10.1117/12.2255740
  21. Naseer Bajwa M, Malik MI, Siddiqui SA, et al. Two-stage framework for op c disc localiza on and glaucoma classifica on in retinal fundus images using deep learning. Published online 2019. doi:10.1186/s12911-019-0842-8
  22. Sreng S, Maneerat N, Hamamoto K, Win KY. Deep Learning for Op c Disc Segmenta on and Glaucoma Diagnosis on Retinal Images. Appl Sci. 2020;10(14):4916. doi:10.3390/app10144916
  23. Chakravarty A, Sivaswamy J. Glaucoma Classifica on with a Fusion of Segmentation and Image-Based Features.; 2016. doi:10.0/Linux-x86_64
  24. Orlando JI, Fu H, Barbossa Breda J, et al. REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med Image Anal. 2020;59:101570. doi:10.1016/j.media.2019.101570
  25. Zhang Z, Yin FS, Liu J, et al. ORIGA-light: An online re nal fundus image database for glaucoma analysis and research. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10. IEEE Computer Society; 2010:3065-3068. doi:10.1109/IEMBS.2010.5626137
  26. Li L, Xu M, Wang X, Jiang L, Liu H. A en on Based Glaucoma Detection: A Large-scale Database and CNN Model. Published online March 26, 2019:1-11. Accessed July 20, 2020. h p://arxiv.org/abs/1903.10831
  27. Sivaswamy J, Chakravarty A, Da Joshi G, Abbas Syed T. A Comprehensive Retinal Image Dataset for the Assessment of Glaucoma from the Op c Nerve. Head Analysis. Vol 2.; 2015.
  28. cvblab/re na_dataset: Re na dataset containing 1) normal 2) cataract 3) glaucoma 4) retina disease. Accessed August 26, 2020. https://github.com/cvblab/ re na_dataset
  29. High-Resolu on Fundus (HRF) Image Database. Accessed August 27, 2020. https://www5.cs.fau.de/research/data/fundus-images/
  30. Dataset-- ODIR-2019 - Grand Challenge. Accessed August 27, 2020. https://odir2019.grand-challenge.org/dataset/
  31. (PDF) Convolu onal Networks for Images, Speech, and Time-Series. Accessed September 11, 2020. https://www.researchgate.net/publication/216792820_Convolutional_Networks_for_Images_Speech_and_Time-Series
  32. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol 2016-December. IEEE Computer Society; 2016:770-778. doi:10.1109/CVPR.2016.90
  33. Microso COCO: Common Objects in Context | Request PDF. Accessed September 11, 2020. https://www.researchgate.net/publication/262049707_Microso_COCO_Common_Objects_in_Context
  34. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Int J Comput Vis. 2016;128(2):336-359. doi:10.1007/s11263-019-01228-7
  35. Glaucoma Screenings: Challenges and Failures. Accessed September 8, 2020. http://www.uniteforsight.org/health-screenings/glaucoma-screening
  36. Glaucoma Screening - EyeWiki. Accessed September 6, 2020. https://eyewiki.aao.org/Glaucoma_Screening
  37. (PDF) Oman Eye Study 2005: validity of screening tests used in the glaucoma survey. Accessed September 6, 2020. https://www.researchgate.net/publication/23930295_Oman_Eye_Study_2005_validity_of_screening_tests_used_in_the_glaucoma_survey
  38. Mohammadi S-F, Mirhadi S, Mehrjardi HZ, et al. An algorithm for glaucoma screening in clinical se ngs and its preliminary performance profile. J Ophthalmic Vis Res. 2013;8(4):314-320. Accessed September 6, 2020. http://www.ncbi.nlm.nih.gov/pubmed/24653818
  39. Healey PR, Lee AJ, Aung T, Wong TY, Mitchell P. Diagnos c accuracy of the Heidelberg Retina Tomograph for Glaucoma: A popula on-based assessment.
  40. Saito H, Tsutsumi T, Araie M, Tomidokoro A, Iwase A. Sensi vity and Specificity of the Heidelberg Re na Tomograph II Version 3.0 in a Popula on-based Study: The Tajimi Study. Ophthalmology. 2009;116(10):1854-1861. doi:10.1016/j.ophtha.2009.03.048
  41. Maul EA, Jampel HD. Glaucoma screening in the real world. Ophthalmology. 2010;117(9):1665-1666. doi:10.1016/j.ophtha.2009.11.001
  42. Christopher M, Nakahara K, Bowd C, et al. Effects of study population, labeling and training on glaucoma detection using deep learning algorithms. doi:10.1167/tvst.9.2.27
  43. Franco, P., Coronado-Gu érrez, D., López, C., & Burgos-Ar zzu, X. (2021). Glaucoma pa ent screening from retinal fundus images via Ar ficial Intelligence